From ac2104ada4c5f43f490a899120ff056036ab2516 Mon Sep 17 00:00:00 2001
From: GitHub Action <action@github.com>
Date: Fri, 28 Jun 2024 09:00:34 +0000
Subject: [PATCH] Update documentation

---
 .../articles_AGBModelVisualisation_31_0.png   |  Bin 758098 -> 768410 bytes
 Python/_images/articles_pdstoolsv3_16_0.svg   |  164 +-
 Python/_images/articles_pdstoolsv3_6_0.svg    |   30 +-
 Python/_images/articles_pdstoolsv3_8_0.svg    |   36 +-
 .../articles/explainability_extract.ipynb.txt |    2 +-
 Python/articles/ADMBinningInsights.html       |  458 ++---
 Python/articles/ADMBinningInsights.ipynb      |  556 +++---
 Python/articles/ADMExplained.html             | 1354 +++++++--------
 Python/articles/ADMExplained.ipynb            | 1494 ++++++++---------
 Python/articles/AGBModelVisualisation.html    |   26 +-
 Python/articles/AGBModelVisualisation.ipynb   |  242 +--
 Python/articles/Example_ADM_Analysis.html     |   56 +-
 Python/articles/Example_ADM_Analysis.ipynb    |  176 +-
 .../articles/Example_Data_Anonymization.html  |   20 +-
 .../articles/Example_Data_Anonymization.ipynb |  348 ++--
 Python/articles/explainability_extract.html   |   38 +-
 Python/articles/explainability_extract.ipynb  |  158 +-
 Python/articles/pdstoolsv3.html               |  404 ++---
 Python/articles/pdstoolsv3.ipynb              | 1016 +++++------
 Python/articles/thompsonsampling.html         |   24 +-
 Python/articles/thompsonsampling.ipynb        |  104 +-
 Python/searchindex.js                         |    2 +-
 22 files changed, 3359 insertions(+), 3349 deletions(-)

diff --git a/Python/_images/articles_AGBModelVisualisation_31_0.png b/Python/_images/articles_AGBModelVisualisation_31_0.png
index fdca71e782721736ea1fdec7d0eba5706c936cb9..ebef6b86dfe0beaa5e514dc3520b4277ee28f87e 100644
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diff --git a/Python/_images/articles_pdstoolsv3_16_0.svg b/Python/_images/articles_pdstoolsv3_16_0.svg
index d5de54f8..8ccc8d8f 100644
--- a/Python/_images/articles_pdstoolsv3_16_0.svg
+++ b/Python/_images/articles_pdstoolsv3_16_0.svg
@@ -1,270 +1,270 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="11280pt" height="906pt" viewBox="0.00 0.00 11280.00 906.00">
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="11238pt" height="906pt" viewBox="0.00 0.00 11238.00 906.00">
 <g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 902)">
 <title>polars_query</title>
-<polygon fill="white" stroke="transparent" points="-4,4 -4,-902 11276,-902 11276,4 -4,4"/>
+<polygon fill="white" stroke="transparent" points="-4,4 -4,-902 11234,-902 11234,4 -4,4"/>
 <!-- p1 -->
 <g id="node1" class="node">
 <title>p1</title>
-<polygon fill="none" stroke="black" points="5243.5,-898 5067.5,-898 5067.5,-845 5243.5,-845 5243.5,-898"/>
-<text text-anchor="middle" x="5155.5" y="-882.8" font-family="Times,serif" font-size="14.00">JOIN INNER</text>
-<text text-anchor="middle" x="5155.5" y="-867.8" font-family="Times,serif" font-size="14.00">left: [col(&quot;ModelID&quot;)];</text>
-<text text-anchor="middle" x="5155.5" y="-852.8" font-family="Times,serif" font-size="14.00">right: [col(&quot;ModelID&quot;)]</text>
+<polygon fill="none" stroke="black" points="5217,-898 5041,-898 5041,-845 5217,-845 5217,-898"/>
+<text text-anchor="middle" x="5129" y="-882.8" font-family="Times,serif" font-size="14.00">JOIN INNER</text>
+<text text-anchor="middle" x="5129" y="-867.8" font-family="Times,serif" font-size="14.00">left: [col(&quot;ModelID&quot;)];</text>
+<text text-anchor="middle" x="5129" y="-852.8" font-family="Times,serif" font-size="14.00">right: [col(&quot;ModelID&quot;)]</text>
 </g>
 <!-- p2 -->
 <g id="node2" class="node">
 <title>p2</title>
-<polygon fill="none" stroke="black" points="5314,-728.5 4471,-728.5 4471,-692.5 5314,-692.5 5314,-728.5"/>
-<text text-anchor="middle" x="4892.5" y="-706.8" font-family="Times,serif" font-size="14.00">FILTER BY [(col(&quot;SnapshotTime&quot;).fill_null_with_strategy()) == (col(&quot;SnapshotTime&quot;).fill_null_with_strategy().max())]</text>
+<polygon fill="none" stroke="black" points="5287.5,-728.5 4444.5,-728.5 4444.5,-692.5 5287.5,-692.5 5287.5,-728.5"/>
+<text text-anchor="middle" x="4866" y="-706.8" font-family="Times,serif" font-size="14.00">FILTER BY [(col(&quot;SnapshotTime&quot;).fill_null_with_strategy()) == (col(&quot;SnapshotTime&quot;).fill_null_with_strategy().max())]</text>
 </g>
 <!-- p1&#45;&#45;p2 -->
 <g id="edge1" class="edge">
 <title>p1--p2</title>
-<path fill="none" stroke="black" d="M5067.29,-852.66C5034.61,-843.24 4998.48,-829.29 4969.5,-809 4938.93,-787.6 4914.51,-750.6 4901.89,-728.85"/>
+<path fill="none" stroke="black" d="M5040.79,-852.66C5008.11,-843.24 4971.98,-829.29 4943,-809 4912.43,-787.6 4888.01,-750.6 4875.39,-728.85"/>
 </g>
 <!-- p9 -->
 <g id="node10" class="node">
 <title>p9</title>
-<polygon fill="none" stroke="black" points="5821,-809 4978,-809 4978,-773 5821,-773 5821,-809"/>
-<text text-anchor="middle" x="5399.5" y="-787.3" font-family="Times,serif" font-size="14.00">FILTER BY [(col(&quot;SnapshotTime&quot;).fill_null_with_strategy()) == (col(&quot;SnapshotTime&quot;).fill_null_with_strategy().max())]</text>
+<polygon fill="none" stroke="black" points="5794.5,-809 4951.5,-809 4951.5,-773 5794.5,-773 5794.5,-809"/>
+<text text-anchor="middle" x="5373" y="-787.3" font-family="Times,serif" font-size="14.00">FILTER BY [(col(&quot;SnapshotTime&quot;).fill_null_with_strategy()) == (col(&quot;SnapshotTime&quot;).fill_null_with_strategy().max())]</text>
 </g>
 <!-- p1&#45;&#45;p9 -->
 <g id="edge9" class="edge">
 <title>p1--p9</title>
-<path fill="none" stroke="black" d="M5235.06,-844.9C5271.63,-833.14 5314.02,-819.5 5346.6,-809.02"/>
+<path fill="none" stroke="black" d="M5208.56,-844.9C5245.13,-833.14 5287.52,-819.5 5320.1,-809.02"/>
 </g>
 <!-- c0 -->
 <g id="node3" class="node">
 <title>c0</title>
-<polygon fill="none" stroke="black" points="5001.5,-502 4869.5,-502 4869.5,-466 5001.5,-466 5001.5,-502"/>
-<text text-anchor="middle" x="4935.5" y="-480.3" font-family="Times,serif" font-size="14.00">CACHE: 1 times</text>
+<polygon fill="none" stroke="black" points="4975,-502 4843,-502 4843,-466 4975,-466 4975,-502"/>
+<text text-anchor="middle" x="4909" y="-480.3" font-family="Times,serif" font-size="14.00">CACHE: 1 times</text>
 </g>
 <!-- p2&#45;&#45;c0 -->
 <g id="edge2" class="edge">
 <title>p2--c0</title>
-<path fill="none" stroke="black" d="M4892.63,-692.13C4893.27,-660.77 4896.44,-592.89 4911.5,-538 4914.92,-525.53 4920.92,-512.28 4926.07,-502.15"/>
+<path fill="none" stroke="black" d="M4866.13,-692.13C4866.77,-660.77 4869.94,-592.89 4885,-538 4888.42,-525.53 4894.42,-512.28 4899.57,-502.15"/>
 </g>
 <!-- p3 -->
 <g id="node4" class="node">
 <title>p3</title>
-<polygon fill="none" stroke="black" points="6532.5,-421.5 6154.5,-421.5 6154.5,-385.5 6532.5,-385.5 6532.5,-421.5"/>
-<text text-anchor="middle" x="6343.5" y="-399.8" font-family="Times,serif" font-size="14.00">FILTER BY col(&quot;Name&quot;).str.contains([String(Visa)])</text>
+<polygon fill="none" stroke="black" points="6506,-421.5 6128,-421.5 6128,-385.5 6506,-385.5 6506,-421.5"/>
+<text text-anchor="middle" x="6317" y="-399.8" font-family="Times,serif" font-size="14.00">FILTER BY col(&quot;Name&quot;).str.contains([String(Visa)])</text>
 </g>
 <!-- c0&#45;&#45;p3 -->
 <g id="edge3" class="edge">
 <title>c0--p3</title>
-<path fill="none" stroke="black" d="M5001.52,-479.32C5206.22,-467.91 5835.53,-432.82 6154.29,-415.05"/>
+<path fill="none" stroke="black" d="M4975.02,-479.32C5179.72,-467.91 5809.03,-432.82 6127.79,-415.05"/>
 </g>
 <!-- p17 -->
 <g id="node18" class="node">
 <title>p17</title>
-<polygon fill="none" stroke="black" points="3709.5,-421.5 3331.5,-421.5 3331.5,-385.5 3709.5,-385.5 3709.5,-421.5"/>
-<text text-anchor="middle" x="3520.5" y="-399.8" font-family="Times,serif" font-size="14.00">FILTER BY col(&quot;Name&quot;).str.contains([String(Visa)])</text>
+<polygon fill="none" stroke="black" points="3694,-421.5 3316,-421.5 3316,-385.5 3694,-385.5 3694,-421.5"/>
+<text text-anchor="middle" x="3505" y="-399.8" font-family="Times,serif" font-size="14.00">FILTER BY col(&quot;Name&quot;).str.contains([String(Visa)])</text>
 </g>
 <!-- c0&#45;&#45;p17 -->
 <g id="edge18" class="edge">
 <title>c0--p17</title>
-<path fill="none" stroke="black" d="M4869.15,-479.32C4663.23,-467.9 4029.73,-432.75 3709.7,-415"/>
+<path fill="none" stroke="black" d="M4842.7,-479.29C4638.19,-467.86 4011.86,-432.84 3694.12,-415.07"/>
 </g>
 <!-- p4 -->
 <g id="node5" class="node">
 <title>p4</title>
-<polygon fill="none" stroke="black" points="11272,-341 5645,-341 5645,-305 11272,-305 11272,-341"/>
-<text text-anchor="middle" x="8458.5" y="-319.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [when([(col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;).is_not_nan()) | (col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;).is_null())]).then(col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;)).otherwise(0.0).alias(&quot;SuccessRate&quot;), when([(col(&quot;Positives&quot;).min()) == (col(&quot;Positives&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;Positives&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_Positives&quot;), when([(col(&quot;ResponseCount&quot;).min()) == (col(&quot;ResponseCount&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;ResponseCount&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_ResponseCount&quot;), [(col(&quot;Positives&quot;)) / (col(&quot;ResponseCount&quot;))].alias(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;)]</text>
+<polygon fill="none" stroke="black" points="11230,-341 5624,-341 5624,-305 11230,-305 11230,-341"/>
+<text text-anchor="middle" x="8427" y="-319.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [when([(col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;).is_not_nan()) | (col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;).is_null())]).then(col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;)).otherwise(0.0).alias(&quot;SuccessRate&quot;), when([(col(&quot;Positives&quot;).min()) == (col(&quot;Positives&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;Positives&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_Positives&quot;), when([(col(&quot;ResponseCount&quot;).min()) == (col(&quot;ResponseCount&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;ResponseCount&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_ResponseCount&quot;), [(col(&quot;Positives&quot;)) / (col(&quot;ResponseCount&quot;))].alias(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;)]</text>
 </g>
 <!-- p3&#45;&#45;p4 -->
 <g id="edge4" class="edge">
 <title>p3--p4</title>
-<path fill="none" stroke="black" d="M6532.59,-395.48C6860.23,-383.32 7542.26,-358.01 8000.32,-341.01"/>
+<path fill="none" stroke="black" d="M6506.11,-395.46C6833.14,-383.3 7513.12,-358 7969.88,-341.01"/>
 </g>
 <!-- p5 -->
 <g id="node6" class="node">
 <title>p5</title>
-<polygon fill="none" stroke="black" points="10863.5,-269 6053.5,-269 6053.5,-233 10863.5,-233 10863.5,-269"/>
-<text text-anchor="middle" x="8458.5" y="-247.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical))]</text>
+<polygon fill="none" stroke="black" points="10832,-269 6022,-269 6022,-233 10832,-233 10832,-269"/>
+<text text-anchor="middle" x="8427" y="-247.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical))]</text>
 </g>
 <!-- p4&#45;&#45;p5 -->
 <g id="edge5" class="edge">
 <title>p4--p5</title>
-<path fill="none" stroke="black" d="M8458.5,-304.7C8458.5,-293.85 8458.5,-279.92 8458.5,-269.1"/>
+<path fill="none" stroke="black" d="M8427,-304.7C8427,-293.85 8427,-279.92 8427,-269.1"/>
 </g>
 <!-- p6 -->
 <g id="node7" class="node">
 <title>p6</title>
-<polygon fill="none" stroke="black" points="8494,-197 8423,-197 8423,-161 8494,-161 8494,-197"/>
-<text text-anchor="middle" x="8458.5" y="-175.3" font-family="Times,serif" font-size="14.00">π 12/12</text>
+<polygon fill="none" stroke="black" points="8462.5,-197 8391.5,-197 8391.5,-161 8462.5,-161 8462.5,-197"/>
+<text text-anchor="middle" x="8427" y="-175.3" font-family="Times,serif" font-size="14.00">π 12/12</text>
 </g>
 <!-- p5&#45;&#45;p6 -->
 <g id="edge6" class="edge">
 <title>p5--p6</title>
-<path fill="none" stroke="black" d="M8458.5,-232.7C8458.5,-221.85 8458.5,-207.92 8458.5,-197.1"/>
+<path fill="none" stroke="black" d="M8427,-232.7C8427,-221.85 8427,-207.92 8427,-197.1"/>
 </g>
 <!-- p7 -->
 <g id="node8" class="node">
 <title>p7</title>
-<polygon fill="none" stroke="black" points="8500.5,-125 8416.5,-125 8416.5,-89 8500.5,-89 8500.5,-125"/>
-<text text-anchor="middle" x="8458.5" y="-103.3" font-family="Times,serif" font-size="14.00">RENAME</text>
+<polygon fill="none" stroke="black" points="8469,-125 8385,-125 8385,-89 8469,-89 8469,-125"/>
+<text text-anchor="middle" x="8427" y="-103.3" font-family="Times,serif" font-size="14.00">RENAME</text>
 </g>
 <!-- p6&#45;&#45;p7 -->
 <g id="edge7" class="edge">
 <title>p6--p7</title>
-<path fill="none" stroke="black" d="M8458.5,-160.7C8458.5,-149.85 8458.5,-135.92 8458.5,-125.1"/>
+<path fill="none" stroke="black" d="M8427,-160.7C8427,-149.85 8427,-135.92 8427,-125.1"/>
 </g>
 <!-- p8 -->
 <g id="node9" class="node">
 <title>p8</title>
-<polygon fill="none" stroke="black" points="8614,-53 8303,-53 8303,0 8614,0 8614,-53"/>
-<text text-anchor="middle" x="8458.5" y="-37.8" font-family="Times,serif" font-size="14.00">TABLE</text>
-<text text-anchor="middle" x="8458.5" y="-22.8" font-family="Times,serif" font-size="14.00">π 12/27;</text>
-<text text-anchor="middle" x="8458.5" y="-7.8" font-family="Times,serif" font-size="14.00">σ col(&quot;Name&quot;).str.contains([String(Visa)])</text>
+<polygon fill="none" stroke="black" points="8582.5,-53 8271.5,-53 8271.5,0 8582.5,0 8582.5,-53"/>
+<text text-anchor="middle" x="8427" y="-37.8" font-family="Times,serif" font-size="14.00">TABLE</text>
+<text text-anchor="middle" x="8427" y="-22.8" font-family="Times,serif" font-size="14.00">π 12/27;</text>
+<text text-anchor="middle" x="8427" y="-7.8" font-family="Times,serif" font-size="14.00">σ col(&quot;Name&quot;).str.contains([String(Visa)])</text>
 </g>
 <!-- p7&#45;&#45;p8 -->
 <g id="edge8" class="edge">
 <title>p7--p8</title>
-<path fill="none" stroke="black" d="M8458.5,-88.97C8458.5,-78.51 8458.5,-64.9 8458.5,-53.16"/>
+<path fill="none" stroke="black" d="M8427,-88.97C8427,-78.51 8427,-64.9 8427,-53.16"/>
 </g>
 <!-- p10 -->
 <g id="node11" class="node">
 <title>p10</title>
-<polygon fill="none" stroke="black" points="5507.5,-737 5331.5,-737 5331.5,-684 5507.5,-684 5507.5,-737"/>
-<text text-anchor="middle" x="5419.5" y="-721.8" font-family="Times,serif" font-size="14.00">JOIN INNER</text>
-<text text-anchor="middle" x="5419.5" y="-706.8" font-family="Times,serif" font-size="14.00">left: [col(&quot;ModelID&quot;)];</text>
-<text text-anchor="middle" x="5419.5" y="-691.8" font-family="Times,serif" font-size="14.00">right: [col(&quot;ModelID&quot;)]</text>
+<polygon fill="none" stroke="black" points="5481,-737 5305,-737 5305,-684 5481,-684 5481,-737"/>
+<text text-anchor="middle" x="5393" y="-721.8" font-family="Times,serif" font-size="14.00">JOIN INNER</text>
+<text text-anchor="middle" x="5393" y="-706.8" font-family="Times,serif" font-size="14.00">left: [col(&quot;ModelID&quot;)];</text>
+<text text-anchor="middle" x="5393" y="-691.8" font-family="Times,serif" font-size="14.00">right: [col(&quot;ModelID&quot;)]</text>
 </g>
 <!-- p9&#45;&#45;p10 -->
 <g id="edge10" class="edge">
 <title>p9--p10</title>
-<path fill="none" stroke="black" d="M5403.84,-772.97C5406.51,-762.51 5409.97,-748.9 5412.96,-737.16"/>
+<path fill="none" stroke="black" d="M5377.34,-772.97C5380.01,-762.51 5383.47,-748.9 5386.46,-737.16"/>
 </g>
 <!-- p11 -->
 <g id="node12" class="node">
 <title>p11</title>
-<polygon fill="none" stroke="black" points="9471.5,-648 5815.5,-648 5815.5,-612 9471.5,-612 9471.5,-648"/>
-<text text-anchor="middle" x="7643.5" y="-626.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [[(col(&quot;BinPositives&quot;)) / (col(&quot;BinResponseCount&quot;))].alias(&quot;BinPropensity&quot;), [([(col(&quot;BinPositives&quot;)) + (0.5)]) / ([(col(&quot;BinResponseCount&quot;)) + (1.0)])].alias(&quot;BinAdjustedPropensity&quot;), when([(col(&quot;__POLARS_CSER_0x3914851859250ee2&quot;).list.length()) &gt; (1)]).then(col(&quot;__POLARS_CSER_0x3914851859250ee2&quot;).list.get([dyn int: 0])).otherwise(String(Primary)).alias(&quot;PredictorCategory&quot;), col(&quot;PredictorName&quot;).strict_cast(String).str.split([String(.)]).alias(&quot;__POLARS_CSER_0x3914851859250ee2&quot;)]</text>
+<polygon fill="none" stroke="black" points="9438.5,-648 5791.5,-648 5791.5,-612 9438.5,-612 9438.5,-648"/>
+<text text-anchor="middle" x="7615" y="-626.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [[(col(&quot;BinPositives&quot;)) / (col(&quot;BinResponseCount&quot;))].alias(&quot;BinPropensity&quot;), [([(col(&quot;BinPositives&quot;)) + (0.5)]) / ([(col(&quot;BinResponseCount&quot;)) + (1.0)])].alias(&quot;BinAdjustedPropensity&quot;), when([(col(&quot;__POLARS_CSER_0x96a44e70932c0d7c&quot;).list.length()) &gt; (1)]).then(col(&quot;__POLARS_CSER_0x96a44e70932c0d7c&quot;).list.get([dyn int: 0])).otherwise(String(Primary)).alias(&quot;PredictorCategory&quot;), col(&quot;PredictorName&quot;).strict_cast(String).str.split([String(.)]).alias(&quot;__POLARS_CSER_0x96a44e70932c0d7c&quot;)]</text>
 </g>
 <!-- p10&#45;&#45;p11 -->
 <g id="edge11" class="edge">
 <title>p10--p11</title>
-<path fill="none" stroke="black" d="M5507.71,-706.39C5779.72,-696.79 6619.77,-667.13 7161.71,-648.01"/>
+<path fill="none" stroke="black" d="M5481.13,-706.39C5752.89,-696.79 6592.2,-667.13 7133.64,-648.01"/>
 </g>
 <!-- p15 -->
 <g id="node16" class="node">
 <title>p15</title>
-<polygon fill="none" stroke="black" points="5225.5,-648 5171.5,-648 5171.5,-612 5225.5,-612 5225.5,-648"/>
-<text text-anchor="middle" x="5198.5" y="-626.3" font-family="Times,serif" font-size="14.00">π 1/1</text>
+<polygon fill="none" stroke="black" points="5199,-648 5145,-648 5145,-612 5199,-612 5199,-648"/>
+<text text-anchor="middle" x="5172" y="-626.3" font-family="Times,serif" font-size="14.00">π 1/1</text>
 </g>
 <!-- p10&#45;&#45;p15 -->
 <g id="edge15" class="edge">
 <title>p10--p15</title>
-<path fill="none" stroke="black" d="M5347.44,-683.9C5305.75,-669.1 5255.71,-651.32 5225.55,-640.61"/>
+<path fill="none" stroke="black" d="M5320.94,-683.9C5279.25,-669.1 5229.21,-651.32 5199.05,-640.61"/>
 </g>
 <!-- p12 -->
 <g id="node13" class="node">
 <title>p12</title>
-<polygon fill="none" stroke="black" points="10938.5,-575 5052.5,-575 5052.5,-539 10938.5,-539 10938.5,-575"/>
-<text text-anchor="middle" x="7995.5" y="-553.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [col(&quot;BinPositives&quot;).strict_cast(Float32), col(&quot;GroupIndex&quot;).strict_cast(UInt32), col(&quot;PredictorName&quot;).strict_cast(Categorical(None, Physical)), col(&quot;BinType&quot;).strict_cast(Categorical(None, Physical)), col(&quot;BinIndex&quot;).strict_cast(UInt16), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;BinResponseCount&quot;).strict_cast(Float32), col(&quot;Type&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;BinNegatives&quot;).strict_cast(Float32), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;EntryType&quot;).strict_cast(Categorical(None, Physical))]</text>
+<polygon fill="none" stroke="black" points="10912,-575 5026,-575 5026,-539 10912,-539 10912,-575"/>
+<text text-anchor="middle" x="7969" y="-553.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [col(&quot;BinResponseCount&quot;).strict_cast(Float32), col(&quot;GroupIndex&quot;).strict_cast(UInt32), col(&quot;PredictorName&quot;).strict_cast(Categorical(None, Physical)), col(&quot;BinIndex&quot;).strict_cast(UInt16), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;BinNegatives&quot;).strict_cast(Float32), col(&quot;EntryType&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Type&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;BinType&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;BinPositives&quot;).strict_cast(Float32)]</text>
 </g>
 <!-- p11&#45;&#45;p12 -->
 <g id="edge12" class="edge">
 <title>p11--p12</title>
-<path fill="none" stroke="black" d="M7727.81,-611.99C7783.24,-600.81 7855.49,-586.24 7910.96,-575.05"/>
+<path fill="none" stroke="black" d="M7699.79,-611.99C7755.53,-600.81 7828.2,-586.24 7883.98,-575.05"/>
 </g>
 <!-- p13 -->
 <g id="node14" class="node">
 <title>p13</title>
-<polygon fill="none" stroke="black" points="8037.5,-502 7953.5,-502 7953.5,-466 8037.5,-466 8037.5,-502"/>
-<text text-anchor="middle" x="7995.5" y="-480.3" font-family="Times,serif" font-size="14.00">RENAME</text>
+<polygon fill="none" stroke="black" points="8011,-502 7927,-502 7927,-466 8011,-466 8011,-502"/>
+<text text-anchor="middle" x="7969" y="-480.3" font-family="Times,serif" font-size="14.00">RENAME</text>
 </g>
 <!-- p12&#45;&#45;p13 -->
 <g id="edge13" class="edge">
 <title>p12--p13</title>
-<path fill="none" stroke="black" d="M7995.5,-538.81C7995.5,-527.65 7995.5,-513.16 7995.5,-502.03"/>
+<path fill="none" stroke="black" d="M7969,-538.81C7969,-527.65 7969,-513.16 7969,-502.03"/>
 </g>
 <!-- p14 -->
 <g id="node15" class="node">
 <title>p14</title>
-<polygon fill="none" stroke="black" points="8033,-430 7958,-430 7958,-377 8033,-377 8033,-430"/>
-<text text-anchor="middle" x="7995.5" y="-414.8" font-family="Times,serif" font-size="14.00">TABLE</text>
-<text text-anchor="middle" x="7995.5" y="-399.8" font-family="Times,serif" font-size="14.00">π 16/36;</text>
-<text text-anchor="middle" x="7995.5" y="-384.8" font-family="Times,serif" font-size="14.00">σ None</text>
+<polygon fill="none" stroke="black" points="8006.5,-430 7931.5,-430 7931.5,-377 8006.5,-377 8006.5,-430"/>
+<text text-anchor="middle" x="7969" y="-414.8" font-family="Times,serif" font-size="14.00">TABLE</text>
+<text text-anchor="middle" x="7969" y="-399.8" font-family="Times,serif" font-size="14.00">π 16/36;</text>
+<text text-anchor="middle" x="7969" y="-384.8" font-family="Times,serif" font-size="14.00">σ None</text>
 </g>
 <!-- p13&#45;&#45;p14 -->
 <g id="edge14" class="edge">
 <title>p13--p14</title>
-<path fill="none" stroke="black" d="M7995.5,-465.97C7995.5,-455.51 7995.5,-441.9 7995.5,-430.16"/>
+<path fill="none" stroke="black" d="M7969,-465.97C7969,-455.51 7969,-441.9 7969,-430.16"/>
 </g>
 <!-- p16 -->
 <g id="node17" class="node">
 <title>p16</title>
-<polygon fill="none" stroke="black" points="5034,-576 4921,-576 4921,-538 5034,-538 5034,-576"/>
-<text text-anchor="middle" x="4977.5" y="-560.8" font-family="Times,serif" font-size="14.00">simple π 1/15</text>
-<text text-anchor="middle" x="4977.5" y="-545.8" font-family="Times,serif" font-size="14.00">[&quot;ModelID&quot;]</text>
+<polygon fill="none" stroke="black" points="5007.5,-576 4894.5,-576 4894.5,-538 5007.5,-538 5007.5,-576"/>
+<text text-anchor="middle" x="4951" y="-560.8" font-family="Times,serif" font-size="14.00">simple π 1/15</text>
+<text text-anchor="middle" x="4951" y="-545.8" font-family="Times,serif" font-size="14.00">[&quot;ModelID&quot;]</text>
 </g>
 <!-- p15&#45;&#45;p16 -->
 <g id="edge16" class="edge">
 <title>p15--p16</title>
-<path fill="none" stroke="black" d="M5171.4,-620.29C5137.28,-609.33 5077.81,-590.23 5033.7,-576.05"/>
+<path fill="none" stroke="black" d="M5144.9,-620.29C5110.78,-609.33 5051.31,-590.23 5007.2,-576.05"/>
 </g>
 <!-- p16&#45;&#45;c0 -->
 <g id="edge17" class="edge">
 <title>p16--c0</title>
-<path fill="none" stroke="black" d="M4966.68,-537.72C4960.17,-526.7 4951.93,-512.78 4945.56,-502"/>
+<path fill="none" stroke="black" d="M4940.18,-537.72C4933.67,-526.7 4925.43,-512.78 4919.06,-502"/>
 </g>
 <!-- p18 -->
 <g id="node19" class="node">
 <title>p18</title>
-<polygon fill="none" stroke="black" points="5627,-341 0,-341 0,-305 5627,-305 5627,-341"/>
-<text text-anchor="middle" x="2813.5" y="-319.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [when([(col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;).is_not_nan()) | (col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;).is_null())]).then(col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;)).otherwise(0.0).alias(&quot;SuccessRate&quot;), when([(col(&quot;Positives&quot;).min()) == (col(&quot;Positives&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;Positives&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_Positives&quot;), when([(col(&quot;ResponseCount&quot;).min()) == (col(&quot;ResponseCount&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;ResponseCount&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_ResponseCount&quot;), [(col(&quot;Positives&quot;)) / (col(&quot;ResponseCount&quot;))].alias(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;)]</text>
+<polygon fill="none" stroke="black" points="5606,-341 0,-341 0,-305 5606,-305 5606,-341"/>
+<text text-anchor="middle" x="2803" y="-319.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [when([(col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;).is_not_nan()) | (col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;).is_null())]).then(col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;)).otherwise(0.0).alias(&quot;SuccessRate&quot;), when([(col(&quot;Positives&quot;).min()) == (col(&quot;Positives&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;Positives&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_Positives&quot;), when([(col(&quot;ResponseCount&quot;).min()) == (col(&quot;ResponseCount&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;ResponseCount&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_ResponseCount&quot;), [(col(&quot;Positives&quot;)) / (col(&quot;ResponseCount&quot;))].alias(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;)]</text>
 </g>
 <!-- p17&#45;&#45;p18 -->
 <g id="edge19" class="edge">
 <title>p17--p18</title>
-<path fill="none" stroke="black" d="M3367.09,-385.47C3248.5,-372.3 3085.12,-354.16 2966.61,-341"/>
+<path fill="none" stroke="black" d="M3352.68,-385.47C3234.93,-372.3 3072.7,-354.16 2955.03,-341"/>
 </g>
 <!-- p19 -->
 <g id="node20" class="node">
 <title>p19</title>
-<polygon fill="none" stroke="black" points="5218.5,-269 408.5,-269 408.5,-233 5218.5,-233 5218.5,-269"/>
-<text text-anchor="middle" x="2813.5" y="-247.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical))]</text>
+<polygon fill="none" stroke="black" points="5208,-269 398,-269 398,-233 5208,-233 5208,-269"/>
+<text text-anchor="middle" x="2803" y="-247.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical))]</text>
 </g>
 <!-- p18&#45;&#45;p19 -->
 <g id="edge20" class="edge">
 <title>p18--p19</title>
-<path fill="none" stroke="black" d="M2813.5,-304.7C2813.5,-293.85 2813.5,-279.92 2813.5,-269.1"/>
+<path fill="none" stroke="black" d="M2803,-304.7C2803,-293.85 2803,-279.92 2803,-269.1"/>
 </g>
 <!-- p20 -->
 <g id="node21" class="node">
 <title>p20</title>
-<polygon fill="none" stroke="black" points="2849,-197 2778,-197 2778,-161 2849,-161 2849,-197"/>
-<text text-anchor="middle" x="2813.5" y="-175.3" font-family="Times,serif" font-size="14.00">π 12/12</text>
+<polygon fill="none" stroke="black" points="2838.5,-197 2767.5,-197 2767.5,-161 2838.5,-161 2838.5,-197"/>
+<text text-anchor="middle" x="2803" y="-175.3" font-family="Times,serif" font-size="14.00">π 12/12</text>
 </g>
 <!-- p19&#45;&#45;p20 -->
 <g id="edge21" class="edge">
 <title>p19--p20</title>
-<path fill="none" stroke="black" d="M2813.5,-232.7C2813.5,-221.85 2813.5,-207.92 2813.5,-197.1"/>
+<path fill="none" stroke="black" d="M2803,-232.7C2803,-221.85 2803,-207.92 2803,-197.1"/>
 </g>
 <!-- p21 -->
 <g id="node22" class="node">
 <title>p21</title>
-<polygon fill="none" stroke="black" points="2855.5,-125 2771.5,-125 2771.5,-89 2855.5,-89 2855.5,-125"/>
-<text text-anchor="middle" x="2813.5" y="-103.3" font-family="Times,serif" font-size="14.00">RENAME</text>
+<polygon fill="none" stroke="black" points="2845,-125 2761,-125 2761,-89 2845,-89 2845,-125"/>
+<text text-anchor="middle" x="2803" y="-103.3" font-family="Times,serif" font-size="14.00">RENAME</text>
 </g>
 <!-- p20&#45;&#45;p21 -->
 <g id="edge22" class="edge">
 <title>p20--p21</title>
-<path fill="none" stroke="black" d="M2813.5,-160.7C2813.5,-149.85 2813.5,-135.92 2813.5,-125.1"/>
+<path fill="none" stroke="black" d="M2803,-160.7C2803,-149.85 2803,-135.92 2803,-125.1"/>
 </g>
 <!-- p22 -->
 <g id="node23" class="node">
 <title>p22</title>
-<polygon fill="none" stroke="black" points="2969,-53 2658,-53 2658,0 2969,0 2969,-53"/>
-<text text-anchor="middle" x="2813.5" y="-37.8" font-family="Times,serif" font-size="14.00">TABLE</text>
-<text text-anchor="middle" x="2813.5" y="-22.8" font-family="Times,serif" font-size="14.00">π 12/27;</text>
-<text text-anchor="middle" x="2813.5" y="-7.8" font-family="Times,serif" font-size="14.00">σ col(&quot;Name&quot;).str.contains([String(Visa)])</text>
+<polygon fill="none" stroke="black" points="2958.5,-53 2647.5,-53 2647.5,0 2958.5,0 2958.5,-53"/>
+<text text-anchor="middle" x="2803" y="-37.8" font-family="Times,serif" font-size="14.00">TABLE</text>
+<text text-anchor="middle" x="2803" y="-22.8" font-family="Times,serif" font-size="14.00">π 12/27;</text>
+<text text-anchor="middle" x="2803" y="-7.8" font-family="Times,serif" font-size="14.00">σ col(&quot;Name&quot;).str.contains([String(Visa)])</text>
 </g>
 <!-- p21&#45;&#45;p22 -->
 <g id="edge23" class="edge">
 <title>p21--p22</title>
-<path fill="none" stroke="black" d="M2813.5,-88.97C2813.5,-78.51 2813.5,-64.9 2813.5,-53.16"/>
+<path fill="none" stroke="black" d="M2803,-88.97C2803,-78.51 2803,-64.9 2803,-53.16"/>
 </g>
 </g>
 </svg>
\ No newline at end of file
diff --git a/Python/_images/articles_pdstoolsv3_6_0.svg b/Python/_images/articles_pdstoolsv3_6_0.svg
index f27f4cf2..979184f3 100644
--- a/Python/_images/articles_pdstoolsv3_6_0.svg
+++ b/Python/_images/articles_pdstoolsv3_6_0.svg
@@ -1,47 +1,47 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="5635pt" height="277pt" viewBox="0.00 0.00 5635.00 277.00">
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="5614pt" height="277pt" viewBox="0.00 0.00 5614.00 277.00">
 <g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 273)">
 <title>polars_query</title>
-<polygon fill="white" stroke="transparent" points="-4,4 -4,-273 5631,-273 5631,4 -4,4"/>
+<polygon fill="white" stroke="transparent" points="-4,4 -4,-273 5610,-273 5610,4 -4,4"/>
 <!-- p1 -->
 <g id="node1" class="node">
 <title>p1</title>
-<polygon fill="none" stroke="black" points="5627,-269 0,-269 0,-233 5627,-233 5627,-269"/>
-<text text-anchor="middle" x="2813.5" y="-247.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [when([(col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;).is_not_nan()) | (col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;).is_null())]).then(col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;)).otherwise(0.0).alias(&quot;SuccessRate&quot;), when([(col(&quot;Positives&quot;).min()) == (col(&quot;Positives&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;Positives&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_Positives&quot;), when([(col(&quot;ResponseCount&quot;).min()) == (col(&quot;ResponseCount&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;ResponseCount&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_ResponseCount&quot;), [(col(&quot;Positives&quot;)) / (col(&quot;ResponseCount&quot;))].alias(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;)]</text>
+<polygon fill="none" stroke="black" points="5606,-269 0,-269 0,-233 5606,-233 5606,-269"/>
+<text text-anchor="middle" x="2803" y="-247.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [when([(col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;).is_not_nan()) | (col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;).is_null())]).then(col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;)).otherwise(0.0).alias(&quot;SuccessRate&quot;), when([(col(&quot;Positives&quot;).min()) == (col(&quot;Positives&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;Positives&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_Positives&quot;), when([(col(&quot;ResponseCount&quot;).min()) == (col(&quot;ResponseCount&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;ResponseCount&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_ResponseCount&quot;), [(col(&quot;Positives&quot;)) / (col(&quot;ResponseCount&quot;))].alias(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;)]</text>
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 <!-- p2 -->
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 <title>p2</title>
-<polygon fill="none" stroke="black" points="5218.5,-197 408.5,-197 408.5,-161 5218.5,-161 5218.5,-197"/>
-<text text-anchor="middle" x="2813.5" y="-175.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical))]</text>
+<polygon fill="none" stroke="black" points="5208,-197 398,-197 398,-161 5208,-161 5208,-197"/>
+<text text-anchor="middle" x="2803" y="-175.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical))]</text>
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 <!-- p1&#45;&#45;p2 -->
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 <title>p1--p2</title>
-<path fill="none" stroke="black" d="M2813.5,-232.7C2813.5,-221.85 2813.5,-207.92 2813.5,-197.1"/>
+<path fill="none" stroke="black" d="M2803,-232.7C2803,-221.85 2803,-207.92 2803,-197.1"/>
 </g>
 <!-- p3 -->
 <g id="node3" class="node">
 <title>p3</title>
-<polygon fill="none" stroke="black" points="2855.5,-125 2771.5,-125 2771.5,-89 2855.5,-89 2855.5,-125"/>
-<text text-anchor="middle" x="2813.5" y="-103.3" font-family="Times,serif" font-size="14.00">RENAME</text>
+<polygon fill="none" stroke="black" points="2845,-125 2761,-125 2761,-89 2845,-89 2845,-125"/>
+<text text-anchor="middle" x="2803" y="-103.3" font-family="Times,serif" font-size="14.00">RENAME</text>
 </g>
 <!-- p2&#45;&#45;p3 -->
 <g id="edge2" class="edge">
 <title>p2--p3</title>
-<path fill="none" stroke="black" d="M2813.5,-160.7C2813.5,-149.85 2813.5,-135.92 2813.5,-125.1"/>
+<path fill="none" stroke="black" d="M2803,-160.7C2803,-149.85 2803,-135.92 2803,-125.1"/>
 </g>
 <!-- p4 -->
 <g id="node4" class="node">
 <title>p4</title>
-<polygon fill="none" stroke="black" points="2851,-53 2776,-53 2776,0 2851,0 2851,-53"/>
-<text text-anchor="middle" x="2813.5" y="-37.8" font-family="Times,serif" font-size="14.00">TABLE</text>
-<text text-anchor="middle" x="2813.5" y="-22.8" font-family="Times,serif" font-size="14.00">π 12/27;</text>
-<text text-anchor="middle" x="2813.5" y="-7.8" font-family="Times,serif" font-size="14.00">σ None</text>
+<polygon fill="none" stroke="black" points="2840.5,-53 2765.5,-53 2765.5,0 2840.5,0 2840.5,-53"/>
+<text text-anchor="middle" x="2803" y="-37.8" font-family="Times,serif" font-size="14.00">TABLE</text>
+<text text-anchor="middle" x="2803" y="-22.8" font-family="Times,serif" font-size="14.00">π 12/27;</text>
+<text text-anchor="middle" x="2803" y="-7.8" font-family="Times,serif" font-size="14.00">σ None</text>
 </g>
 <!-- p3&#45;&#45;p4 -->
 <g id="edge3" class="edge">
 <title>p3--p4</title>
-<path fill="none" stroke="black" d="M2813.5,-88.97C2813.5,-78.51 2813.5,-64.9 2813.5,-53.16"/>
+<path fill="none" stroke="black" d="M2803,-88.97C2803,-78.51 2803,-64.9 2803,-53.16"/>
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diff --git a/Python/_images/articles_pdstoolsv3_8_0.svg b/Python/_images/articles_pdstoolsv3_8_0.svg
index e238fc2c..ede446d0 100644
--- a/Python/_images/articles_pdstoolsv3_8_0.svg
+++ b/Python/_images/articles_pdstoolsv3_8_0.svg
@@ -1,58 +1,58 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="5635pt" height="349pt" viewBox="0.00 0.00 5635.00 349.00">
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="5614pt" height="349pt" viewBox="0.00 0.00 5614.00 349.00">
 <g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 345)">
 <title>polars_query</title>
-<polygon fill="white" stroke="transparent" points="-4,4 -4,-345 5631,-345 5631,4 -4,4"/>
+<polygon fill="white" stroke="transparent" points="-4,4 -4,-345 5610,-345 5610,4 -4,4"/>
 <!-- p1 -->
 <g id="node1" class="node">
 <title>p1</title>
-<polygon fill="none" stroke="black" points="3002.5,-341 2624.5,-341 2624.5,-305 3002.5,-305 3002.5,-341"/>
-<text text-anchor="middle" x="2813.5" y="-319.3" font-family="Times,serif" font-size="14.00">FILTER BY col(&quot;Name&quot;).str.contains([String(Visa)])</text>
+<polygon fill="none" stroke="black" points="2992,-341 2614,-341 2614,-305 2992,-305 2992,-341"/>
+<text text-anchor="middle" x="2803" y="-319.3" font-family="Times,serif" font-size="14.00">FILTER BY col(&quot;Name&quot;).str.contains([String(Visa)])</text>
 </g>
 <!-- p2 -->
 <g id="node2" class="node">
 <title>p2</title>
-<polygon fill="none" stroke="black" points="5627,-269 0,-269 0,-233 5627,-233 5627,-269"/>
-<text text-anchor="middle" x="2813.5" y="-247.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [when([(col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;).is_not_nan()) | (col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;).is_null())]).then(col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;)).otherwise(0.0).alias(&quot;SuccessRate&quot;), when([(col(&quot;Positives&quot;).min()) == (col(&quot;Positives&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;Positives&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_Positives&quot;), when([(col(&quot;ResponseCount&quot;).min()) == (col(&quot;ResponseCount&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;ResponseCount&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_ResponseCount&quot;), [(col(&quot;Positives&quot;)) / (col(&quot;ResponseCount&quot;))].alias(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;)]</text>
+<polygon fill="none" stroke="black" points="5606,-269 0,-269 0,-233 5606,-233 5606,-269"/>
+<text text-anchor="middle" x="2803" y="-247.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [when([(col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;).is_not_nan()) | (col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;).is_null())]).then(col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;)).otherwise(0.0).alias(&quot;SuccessRate&quot;), when([(col(&quot;Positives&quot;).min()) == (col(&quot;Positives&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;Positives&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_Positives&quot;), when([(col(&quot;ResponseCount&quot;).min()) == (col(&quot;ResponseCount&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;ResponseCount&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_ResponseCount&quot;), [(col(&quot;Positives&quot;)) / (col(&quot;ResponseCount&quot;))].alias(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;)]</text>
 </g>
 <!-- p1&#45;&#45;p2 -->
 <g id="edge1" class="edge">
 <title>p1--p2</title>
-<path fill="none" stroke="black" d="M2813.5,-304.7C2813.5,-293.85 2813.5,-279.92 2813.5,-269.1"/>
+<path fill="none" stroke="black" d="M2803,-304.7C2803,-293.85 2803,-279.92 2803,-269.1"/>
 </g>
 <!-- p3 -->
 <g id="node3" class="node">
 <title>p3</title>
-<polygon fill="none" stroke="black" points="5218.5,-197 408.5,-197 408.5,-161 5218.5,-161 5218.5,-197"/>
-<text text-anchor="middle" x="2813.5" y="-175.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical))]</text>
+<polygon fill="none" stroke="black" points="5208,-197 398,-197 398,-161 5208,-161 5208,-197"/>
+<text text-anchor="middle" x="2803" y="-175.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical))]</text>
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 <!-- p2&#45;&#45;p3 -->
 <g id="edge2" class="edge">
 <title>p2--p3</title>
-<path fill="none" stroke="black" d="M2813.5,-232.7C2813.5,-221.85 2813.5,-207.92 2813.5,-197.1"/>
+<path fill="none" stroke="black" d="M2803,-232.7C2803,-221.85 2803,-207.92 2803,-197.1"/>
 </g>
 <!-- p4 -->
 <g id="node4" class="node">
 <title>p4</title>
-<polygon fill="none" stroke="black" points="2855.5,-125 2771.5,-125 2771.5,-89 2855.5,-89 2855.5,-125"/>
-<text text-anchor="middle" x="2813.5" y="-103.3" font-family="Times,serif" font-size="14.00">RENAME</text>
+<polygon fill="none" stroke="black" points="2845,-125 2761,-125 2761,-89 2845,-89 2845,-125"/>
+<text text-anchor="middle" x="2803" y="-103.3" font-family="Times,serif" font-size="14.00">RENAME</text>
 </g>
 <!-- p3&#45;&#45;p4 -->
 <g id="edge3" class="edge">
 <title>p3--p4</title>
-<path fill="none" stroke="black" d="M2813.5,-160.7C2813.5,-149.85 2813.5,-135.92 2813.5,-125.1"/>
+<path fill="none" stroke="black" d="M2803,-160.7C2803,-149.85 2803,-135.92 2803,-125.1"/>
 </g>
 <!-- p5 -->
 <g id="node5" class="node">
 <title>p5</title>
-<polygon fill="none" stroke="black" points="2969,-53 2658,-53 2658,0 2969,0 2969,-53"/>
-<text text-anchor="middle" x="2813.5" y="-37.8" font-family="Times,serif" font-size="14.00">TABLE</text>
-<text text-anchor="middle" x="2813.5" y="-22.8" font-family="Times,serif" font-size="14.00">π 12/27;</text>
-<text text-anchor="middle" x="2813.5" y="-7.8" font-family="Times,serif" font-size="14.00">σ col(&quot;Name&quot;).str.contains([String(Visa)])</text>
+<polygon fill="none" stroke="black" points="2958.5,-53 2647.5,-53 2647.5,0 2958.5,0 2958.5,-53"/>
+<text text-anchor="middle" x="2803" y="-37.8" font-family="Times,serif" font-size="14.00">TABLE</text>
+<text text-anchor="middle" x="2803" y="-22.8" font-family="Times,serif" font-size="14.00">π 12/27;</text>
+<text text-anchor="middle" x="2803" y="-7.8" font-family="Times,serif" font-size="14.00">σ col(&quot;Name&quot;).str.contains([String(Visa)])</text>
 </g>
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 <g id="edge4" class="edge">
 <title>p4--p5</title>
-<path fill="none" stroke="black" d="M2813.5,-88.97C2813.5,-78.51 2813.5,-64.9 2813.5,-53.16"/>
+<path fill="none" stroke="black" d="M2803,-88.97C2803,-78.51 2803,-64.9 2803,-53.16"/>
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diff --git a/Python/_sources/articles/explainability_extract.ipynb.txt b/Python/_sources/articles/explainability_extract.ipynb.txt
index 4f734282..568b2647 100644
--- a/Python/_sources/articles/explainability_extract.ipynb.txt
+++ b/Python/_sources/articles/explainability_extract.ipynb.txt
@@ -10,7 +10,7 @@
                 "\n",
                 "__2024-06-11__\n",
                 "\n",
-                "Welcome to the Explainability Extract Demo. This notebook is designed to guide you through the analysis of Explainability Extract data using the DecisionData class of pdstools library. At this point, the dataset is particularly targeted on the \"Arbitration\" stage, but we have intentions to widen the data scope in subsequent iterations.\n",
+                "Welcome to the Explainability Extract Demo. This notebook is designed to guide you through the analysis of Explainability Extract v1 dataset using the DecisionData class of pdstools library. At this point, the dataset is particularly targeted on the \"Arbitration\" stage, but we have intentions to widen the data scope in subsequent iterations. This dataset can be extracted from Infinity 24.1 and its preceding versions.\n",
                 "\n",
                 "We developed this notebook with a dual purpose. Firstly, we aim to familiarize you with the various functions and visualizations available in the DecisionData class. You'll learn how to aggregate and visualize data in ways that are meaningful and insightful for your specific use cases.\n",
                 "\n",
diff --git a/Python/articles/ADMBinningInsights.html b/Python/articles/ADMBinningInsights.html
index 86d02256..70ebd92e 100644
--- a/Python/articles/ADMBinningInsights.html
+++ b/Python/articles/ADMBinningInsights.html
@@ -443,9 +443,9 @@ <h1>Insights from ADM Models<a class="headerlink" href="#Insights-from-ADM-Model
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@@ -712,261 +712,261 @@ <h2>Rolling up individual binning<a class="headerlink" href="#Rolling-up-individ
 <div class="output_area rendered_html docutils container">
 <style type="text/css">
 </style>
-<table id="T_ac3fc">
+<table id="T_7bb5b">
   <thead>
     <tr>
-      <th id="T_ac3fc_level0_col0" class="col_heading level0 col0" >PredictorName</th>
-      <th id="T_ac3fc_level0_col1" class="col_heading level0 col1" >BinIndex</th>
-      <th id="T_ac3fc_level0_col2" class="col_heading level0 col2" >BinLowerBound</th>
-      <th id="T_ac3fc_level0_col3" class="col_heading level0 col3" >BinUpperBound</th>
-      <th id="T_ac3fc_level0_col4" class="col_heading level0 col4" >BinSymbol</th>
-      <th id="T_ac3fc_level0_col5" class="col_heading level0 col5" >Lift</th>
-      <th id="T_ac3fc_level0_col6" class="col_heading level0 col6" >BinResponses</th>
-      <th id="T_ac3fc_level0_col7" class="col_heading level0 col7" >BinCoverage</th>
-      <th id="T_ac3fc_level0_col8" class="col_heading level0 col8" >Models</th>
-      <th id="T_ac3fc_level0_col9" class="col_heading level0 col9" >Group</th>
+      <th id="T_7bb5b_level0_col0" class="col_heading level0 col0" >PredictorName</th>
+      <th id="T_7bb5b_level0_col1" class="col_heading level0 col1" >BinIndex</th>
+      <th id="T_7bb5b_level0_col2" class="col_heading level0 col2" >BinLowerBound</th>
+      <th id="T_7bb5b_level0_col3" class="col_heading level0 col3" >BinUpperBound</th>
+      <th id="T_7bb5b_level0_col4" class="col_heading level0 col4" >BinSymbol</th>
+      <th id="T_7bb5b_level0_col5" class="col_heading level0 col5" >Lift</th>
+      <th id="T_7bb5b_level0_col6" class="col_heading level0 col6" >BinResponses</th>
+      <th id="T_7bb5b_level0_col7" class="col_heading level0 col7" >BinCoverage</th>
+      <th id="T_7bb5b_level0_col8" class="col_heading level0 col8" >Models</th>
+      <th id="T_7bb5b_level0_col9" class="col_heading level0 col9" >Group</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <td id="T_ac3fc_row0_col0" class="data row0 col0" >Customer.Age</td>
-      <td id="T_ac3fc_row0_col1" class="data row0 col1" >1</td>
-      <td id="T_ac3fc_row0_col2" class="data row0 col2" >20.000000</td>
-      <td id="T_ac3fc_row0_col3" class="data row0 col3" >32.000000</td>
-      <td id="T_ac3fc_row0_col4" class="data row0 col4" ><32.0</td>
-      <td id="T_ac3fc_row0_col5" class="data row0 col5" >0.229643</td>
-      <td id="T_ac3fc_row0_col6" class="data row0 col6" >4398.940698</td>
-      <td id="T_ac3fc_row0_col7" class="data row0 col7" >10.000000</td>
-      <td id="T_ac3fc_row0_col8" class="data row0 col8" >10</td>
-      <td id="T_ac3fc_row0_col9" class="data row0 col9" >AutoLoans</td>
+      <td id="T_7bb5b_row0_col0" class="data row0 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row0_col1" class="data row0 col1" >1</td>
+      <td id="T_7bb5b_row0_col2" class="data row0 col2" >20.000000</td>
+      <td id="T_7bb5b_row0_col3" class="data row0 col3" >32.000000</td>
+      <td id="T_7bb5b_row0_col4" class="data row0 col4" ><32.0</td>
+      <td id="T_7bb5b_row0_col5" class="data row0 col5" >0.229643</td>
+      <td id="T_7bb5b_row0_col6" class="data row0 col6" >4398.940698</td>
+      <td id="T_7bb5b_row0_col7" class="data row0 col7" >10.000000</td>
+      <td id="T_7bb5b_row0_col8" class="data row0 col8" >10</td>
+      <td id="T_7bb5b_row0_col9" class="data row0 col9" >AutoLoans</td>
     </tr>
     <tr>
-      <td id="T_ac3fc_row1_col0" class="data row1 col0" >Customer.Age</td>
-      <td id="T_ac3fc_row1_col1" class="data row1 col1" >2</td>
-      <td id="T_ac3fc_row1_col2" class="data row1 col2" >32.000000</td>
-      <td id="T_ac3fc_row1_col3" class="data row1 col3" >44.000000</td>
-      <td id="T_ac3fc_row1_col4" class="data row1 col4" ><44.0</td>
-      <td id="T_ac3fc_row1_col5" class="data row1 col5" >-0.261505</td>
-      <td id="T_ac3fc_row1_col6" class="data row1 col6" >8280.450496</td>
-      <td id="T_ac3fc_row1_col7" class="data row1 col7" >10.000000</td>
-      <td id="T_ac3fc_row1_col8" class="data row1 col8" >10</td>
-      <td id="T_ac3fc_row1_col9" class="data row1 col9" >AutoLoans</td>
+      <td id="T_7bb5b_row1_col0" class="data row1 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row1_col1" class="data row1 col1" >2</td>
+      <td id="T_7bb5b_row1_col2" class="data row1 col2" >32.000000</td>
+      <td id="T_7bb5b_row1_col3" class="data row1 col3" >44.000000</td>
+      <td id="T_7bb5b_row1_col4" class="data row1 col4" ><44.0</td>
+      <td id="T_7bb5b_row1_col5" class="data row1 col5" >-0.261505</td>
+      <td id="T_7bb5b_row1_col6" class="data row1 col6" >8280.450496</td>
+      <td id="T_7bb5b_row1_col7" class="data row1 col7" >10.000000</td>
+      <td id="T_7bb5b_row1_col8" class="data row1 col8" >10</td>
+      <td id="T_7bb5b_row1_col9" class="data row1 col9" >AutoLoans</td>
     </tr>
     <tr>
-      <td id="T_ac3fc_row2_col0" class="data row2 col0" >Customer.Age</td>
-      <td id="T_ac3fc_row2_col1" class="data row2 col1" >3</td>
-      <td id="T_ac3fc_row2_col2" class="data row2 col2" >44.000000</td>
-      <td id="T_ac3fc_row2_col3" class="data row2 col3" >56.000000</td>
-      <td id="T_ac3fc_row2_col4" class="data row2 col4" ><56.0</td>
-      <td id="T_ac3fc_row2_col5" class="data row2 col5" >0.281045</td>
-      <td id="T_ac3fc_row2_col6" class="data row2 col6" >4540.748856</td>
-      <td id="T_ac3fc_row2_col7" class="data row2 col7" >10.000000</td>
-      <td id="T_ac3fc_row2_col8" class="data row2 col8" >10</td>
-      <td id="T_ac3fc_row2_col9" class="data row2 col9" >AutoLoans</td>
+      <td id="T_7bb5b_row2_col0" class="data row2 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row2_col1" class="data row2 col1" >3</td>
+      <td id="T_7bb5b_row2_col2" class="data row2 col2" >44.000000</td>
+      <td id="T_7bb5b_row2_col3" class="data row2 col3" >56.000000</td>
+      <td id="T_7bb5b_row2_col4" class="data row2 col4" ><56.0</td>
+      <td id="T_7bb5b_row2_col5" class="data row2 col5" >0.281045</td>
+      <td id="T_7bb5b_row2_col6" class="data row2 col6" >4540.748856</td>
+      <td id="T_7bb5b_row2_col7" class="data row2 col7" >10.000000</td>
+      <td id="T_7bb5b_row2_col8" class="data row2 col8" >10</td>
+      <td id="T_7bb5b_row2_col9" class="data row2 col9" >AutoLoans</td>
     </tr>
     <tr>
-      <td id="T_ac3fc_row3_col0" class="data row3 col0" >Customer.Age</td>
-      <td id="T_ac3fc_row3_col1" class="data row3 col1" >4</td>
-      <td id="T_ac3fc_row3_col2" class="data row3 col2" >56.000000</td>
-      <td id="T_ac3fc_row3_col3" class="data row3 col3" >68.000000</td>
-      <td id="T_ac3fc_row3_col4" class="data row3 col4" ><68.0</td>
-      <td id="T_ac3fc_row3_col5" class="data row3 col5" >0.501178</td>
-      <td id="T_ac3fc_row3_col6" class="data row3 col6" >1272.362768</td>
-      <td id="T_ac3fc_row3_col7" class="data row3 col7" >10.000000</td>
-      <td id="T_ac3fc_row3_col8" class="data row3 col8" >10</td>
-      <td id="T_ac3fc_row3_col9" class="data row3 col9" >AutoLoans</td>
+      <td id="T_7bb5b_row3_col0" class="data row3 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row3_col1" class="data row3 col1" >4</td>
+      <td id="T_7bb5b_row3_col2" class="data row3 col2" >56.000000</td>
+      <td id="T_7bb5b_row3_col3" class="data row3 col3" >68.000000</td>
+      <td id="T_7bb5b_row3_col4" class="data row3 col4" ><68.0</td>
+      <td id="T_7bb5b_row3_col5" class="data row3 col5" >0.501178</td>
+      <td id="T_7bb5b_row3_col6" class="data row3 col6" >1272.362768</td>
+      <td id="T_7bb5b_row3_col7" class="data row3 col7" >10.000000</td>
+      <td id="T_7bb5b_row3_col8" class="data row3 col8" >10</td>
+      <td id="T_7bb5b_row3_col9" class="data row3 col9" >AutoLoans</td>
     </tr>
     <tr>
-      <td id="T_ac3fc_row4_col0" class="data row4 col0" >Customer.Age</td>
-      <td id="T_ac3fc_row4_col1" class="data row4 col1" >5</td>
-      <td id="T_ac3fc_row4_col2" class="data row4 col2" >68.000000</td>
-      <td id="T_ac3fc_row4_col3" class="data row4 col3" >80.000000</td>
-      <td id="T_ac3fc_row4_col4" class="data row4 col4" ><80.0</td>
-      <td id="T_ac3fc_row4_col5" class="data row4 col5" >0.510309</td>
-      <td id="T_ac3fc_row4_col6" class="data row4 col6" >965.237926</td>
-      <td id="T_ac3fc_row4_col7" class="data row4 col7" >8.166667</td>
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-      <td id="T_ac3fc_row4_col9" class="data row4 col9" >AutoLoans</td>
+      <td id="T_7bb5b_row4_col0" class="data row4 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row4_col1" class="data row4 col1" >5</td>
+      <td id="T_7bb5b_row4_col2" class="data row4 col2" >68.000000</td>
+      <td id="T_7bb5b_row4_col3" class="data row4 col3" >80.000000</td>
+      <td id="T_7bb5b_row4_col4" class="data row4 col4" ><80.0</td>
+      <td id="T_7bb5b_row4_col5" class="data row4 col5" >0.510309</td>
+      <td id="T_7bb5b_row4_col6" class="data row4 col6" >965.237926</td>
+      <td id="T_7bb5b_row4_col7" class="data row4 col7" >8.166667</td>
+      <td id="T_7bb5b_row4_col8" class="data row4 col8" >10</td>
+      <td id="T_7bb5b_row4_col9" class="data row4 col9" >AutoLoans</td>
     </tr>
     <tr>
-      <td id="T_ac3fc_row5_col0" class="data row5 col0" >Customer.Age</td>
-      <td id="T_ac3fc_row5_col1" class="data row5 col1" >1</td>
-      <td id="T_ac3fc_row5_col2" class="data row5 col2" >20.000000</td>
-      <td id="T_ac3fc_row5_col3" class="data row5 col3" >32.000000</td>
-      <td id="T_ac3fc_row5_col4" class="data row5 col4" ><32.0</td>
-      <td id="T_ac3fc_row5_col5" class="data row5 col5" >0.110590</td>
-      <td id="T_ac3fc_row5_col6" class="data row5 col6" >995.724951</td>
-      <td id="T_ac3fc_row5_col7" class="data row5 col7" >7.000000</td>
-      <td id="T_ac3fc_row5_col8" class="data row5 col8" >7</td>
-      <td id="T_ac3fc_row5_col9" class="data row5 col9" >HomeLoans</td>
+      <td id="T_7bb5b_row5_col0" class="data row5 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row5_col1" class="data row5 col1" >1</td>
+      <td id="T_7bb5b_row5_col2" class="data row5 col2" >20.000000</td>
+      <td id="T_7bb5b_row5_col3" class="data row5 col3" >32.000000</td>
+      <td id="T_7bb5b_row5_col4" class="data row5 col4" ><32.0</td>
+      <td id="T_7bb5b_row5_col5" class="data row5 col5" >0.110590</td>
+      <td id="T_7bb5b_row5_col6" class="data row5 col6" >995.724951</td>
+      <td id="T_7bb5b_row5_col7" class="data row5 col7" >7.000000</td>
+      <td id="T_7bb5b_row5_col8" class="data row5 col8" >7</td>
+      <td id="T_7bb5b_row5_col9" class="data row5 col9" >HomeLoans</td>
     </tr>
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-      <td id="T_ac3fc_row6_col0" class="data row6 col0" >Customer.Age</td>
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-      <td id="T_ac3fc_row6_col3" class="data row6 col3" >44.000000</td>
-      <td id="T_ac3fc_row6_col4" class="data row6 col4" ><44.0</td>
-      <td id="T_ac3fc_row6_col5" class="data row6 col5" >-0.078623</td>
-      <td id="T_ac3fc_row6_col6" class="data row6 col6" >1975.071325</td>
-      <td id="T_ac3fc_row6_col7" class="data row6 col7" >7.000000</td>
-      <td id="T_ac3fc_row6_col8" class="data row6 col8" >7</td>
-      <td id="T_ac3fc_row6_col9" class="data row6 col9" >HomeLoans</td>
+      <td id="T_7bb5b_row6_col0" class="data row6 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row6_col1" class="data row6 col1" >2</td>
+      <td id="T_7bb5b_row6_col2" class="data row6 col2" >32.000000</td>
+      <td id="T_7bb5b_row6_col3" class="data row6 col3" >44.000000</td>
+      <td id="T_7bb5b_row6_col4" class="data row6 col4" ><44.0</td>
+      <td id="T_7bb5b_row6_col5" class="data row6 col5" >-0.078623</td>
+      <td id="T_7bb5b_row6_col6" class="data row6 col6" >1975.071325</td>
+      <td id="T_7bb5b_row6_col7" class="data row6 col7" >7.000000</td>
+      <td id="T_7bb5b_row6_col8" class="data row6 col8" >7</td>
+      <td id="T_7bb5b_row6_col9" class="data row6 col9" >HomeLoans</td>
     </tr>
     <tr>
-      <td id="T_ac3fc_row7_col0" class="data row7 col0" >Customer.Age</td>
-      <td id="T_ac3fc_row7_col1" class="data row7 col1" >3</td>
-      <td id="T_ac3fc_row7_col2" class="data row7 col2" >44.000000</td>
-      <td id="T_ac3fc_row7_col3" class="data row7 col3" >56.000000</td>
-      <td id="T_ac3fc_row7_col4" class="data row7 col4" ><56.0</td>
-      <td id="T_ac3fc_row7_col5" class="data row7 col5" >0.047313</td>
-      <td id="T_ac3fc_row7_col6" class="data row7 col6" >1350.110244</td>
-      <td id="T_ac3fc_row7_col7" class="data row7 col7" >7.000000</td>
-      <td id="T_ac3fc_row7_col8" class="data row7 col8" >7</td>
-      <td id="T_ac3fc_row7_col9" class="data row7 col9" >HomeLoans</td>
+      <td id="T_7bb5b_row7_col0" class="data row7 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row7_col1" class="data row7 col1" >3</td>
+      <td id="T_7bb5b_row7_col2" class="data row7 col2" >44.000000</td>
+      <td id="T_7bb5b_row7_col3" class="data row7 col3" >56.000000</td>
+      <td id="T_7bb5b_row7_col4" class="data row7 col4" ><56.0</td>
+      <td id="T_7bb5b_row7_col5" class="data row7 col5" >0.047313</td>
+      <td id="T_7bb5b_row7_col6" class="data row7 col6" >1350.110244</td>
+      <td id="T_7bb5b_row7_col7" class="data row7 col7" >7.000000</td>
+      <td id="T_7bb5b_row7_col8" class="data row7 col8" >7</td>
+      <td id="T_7bb5b_row7_col9" class="data row7 col9" >HomeLoans</td>
     </tr>
     <tr>
-      <td id="T_ac3fc_row8_col0" class="data row8 col0" >Customer.Age</td>
-      <td id="T_ac3fc_row8_col1" class="data row8 col1" >4</td>
-      <td id="T_ac3fc_row8_col2" class="data row8 col2" >56.000000</td>
-      <td id="T_ac3fc_row8_col3" class="data row8 col3" >68.000000</td>
-      <td id="T_ac3fc_row8_col4" class="data row8 col4" ><68.0</td>
-      <td id="T_ac3fc_row8_col5" class="data row8 col5" >0.145987</td>
-      <td id="T_ac3fc_row8_col6" class="data row8 col6" >251.370112</td>
-      <td id="T_ac3fc_row8_col7" class="data row8 col7" >7.000000</td>
-      <td id="T_ac3fc_row8_col8" class="data row8 col8" >7</td>
-      <td id="T_ac3fc_row8_col9" class="data row8 col9" >HomeLoans</td>
+      <td id="T_7bb5b_row8_col0" class="data row8 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row8_col1" class="data row8 col1" >4</td>
+      <td id="T_7bb5b_row8_col2" class="data row8 col2" >56.000000</td>
+      <td id="T_7bb5b_row8_col3" class="data row8 col3" >68.000000</td>
+      <td id="T_7bb5b_row8_col4" class="data row8 col4" ><68.0</td>
+      <td id="T_7bb5b_row8_col5" class="data row8 col5" >0.145987</td>
+      <td id="T_7bb5b_row8_col6" class="data row8 col6" >251.370112</td>
+      <td id="T_7bb5b_row8_col7" class="data row8 col7" >7.000000</td>
+      <td id="T_7bb5b_row8_col8" class="data row8 col8" >7</td>
+      <td id="T_7bb5b_row8_col9" class="data row8 col9" >HomeLoans</td>
     </tr>
     <tr>
-      <td id="T_ac3fc_row9_col0" class="data row9 col0" >Customer.Age</td>
-      <td id="T_ac3fc_row9_col1" class="data row9 col1" >5</td>
-      <td id="T_ac3fc_row9_col2" class="data row9 col2" >68.000000</td>
-      <td id="T_ac3fc_row9_col3" class="data row9 col3" >80.000000</td>
-      <td id="T_ac3fc_row9_col4" class="data row9 col4" ><80.0</td>
-      <td id="T_ac3fc_row9_col5" class="data row9 col5" >0.167526</td>
-      <td id="T_ac3fc_row9_col6" class="data row9 col6" >209.475093</td>
-      <td id="T_ac3fc_row9_col7" class="data row9 col7" >5.083333</td>
-      <td id="T_ac3fc_row9_col8" class="data row9 col8" >7</td>
-      <td id="T_ac3fc_row9_col9" class="data row9 col9" >HomeLoans</td>
+      <td id="T_7bb5b_row9_col0" class="data row9 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row9_col1" class="data row9 col1" >5</td>
+      <td id="T_7bb5b_row9_col2" class="data row9 col2" >68.000000</td>
+      <td id="T_7bb5b_row9_col3" class="data row9 col3" >80.000000</td>
+      <td id="T_7bb5b_row9_col4" class="data row9 col4" ><80.0</td>
+      <td id="T_7bb5b_row9_col5" class="data row9 col5" >0.167526</td>
+      <td id="T_7bb5b_row9_col6" class="data row9 col6" >209.475093</td>
+      <td id="T_7bb5b_row9_col7" class="data row9 col7" >5.083333</td>
+      <td id="T_7bb5b_row9_col8" class="data row9 col8" >7</td>
+      <td id="T_7bb5b_row9_col9" class="data row9 col9" >HomeLoans</td>
     </tr>
     <tr>
-      <td id="T_ac3fc_row10_col0" class="data row10 col0" >Customer.Age</td>
-      <td id="T_ac3fc_row10_col1" class="data row10 col1" >1</td>
-      <td id="T_ac3fc_row10_col2" class="data row10 col2" >20.000000</td>
-      <td id="T_ac3fc_row10_col3" class="data row10 col3" >32.000000</td>
-      <td id="T_ac3fc_row10_col4" class="data row10 col4" ><32.0</td>
-      <td id="T_ac3fc_row10_col5" class="data row10 col5" >0.038873</td>
-      <td id="T_ac3fc_row10_col6" class="data row10 col6" >247.676726</td>
-      <td id="T_ac3fc_row10_col7" class="data row10 col7" >1.000000</td>
-      <td id="T_ac3fc_row10_col8" class="data row10 col8" >1</td>
-      <td id="T_ac3fc_row10_col9" class="data row10 col9" >CreditCards</td>
+      <td id="T_7bb5b_row10_col0" class="data row10 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row10_col1" class="data row10 col1" >1</td>
+      <td id="T_7bb5b_row10_col2" class="data row10 col2" >20.000000</td>
+      <td id="T_7bb5b_row10_col3" class="data row10 col3" >32.000000</td>
+      <td id="T_7bb5b_row10_col4" class="data row10 col4" ><32.0</td>
+      <td id="T_7bb5b_row10_col5" class="data row10 col5" >0.038873</td>
+      <td id="T_7bb5b_row10_col6" class="data row10 col6" >247.676726</td>
+      <td id="T_7bb5b_row10_col7" class="data row10 col7" >1.000000</td>
+      <td id="T_7bb5b_row10_col8" class="data row10 col8" >1</td>
+      <td id="T_7bb5b_row10_col9" class="data row10 col9" >CreditCards</td>
     </tr>
     <tr>
-      <td id="T_ac3fc_row11_col0" class="data row11 col0" >Customer.Age</td>
-      <td id="T_ac3fc_row11_col1" class="data row11 col1" >2</td>
-      <td id="T_ac3fc_row11_col2" class="data row11 col2" >32.000000</td>
-      <td id="T_ac3fc_row11_col3" class="data row11 col3" >44.000000</td>
-      <td id="T_ac3fc_row11_col4" class="data row11 col4" ><44.0</td>
-      <td id="T_ac3fc_row11_col5" class="data row11 col5" >-0.598058</td>
-      <td id="T_ac3fc_row11_col6" class="data row11 col6" >324.579816</td>
-      <td id="T_ac3fc_row11_col7" class="data row11 col7" >1.000000</td>
-      <td id="T_ac3fc_row11_col8" class="data row11 col8" >1</td>
-      <td id="T_ac3fc_row11_col9" class="data row11 col9" >CreditCards</td>
+      <td id="T_7bb5b_row11_col0" class="data row11 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row11_col1" class="data row11 col1" >2</td>
+      <td id="T_7bb5b_row11_col2" class="data row11 col2" >32.000000</td>
+      <td id="T_7bb5b_row11_col3" class="data row11 col3" >44.000000</td>
+      <td id="T_7bb5b_row11_col4" class="data row11 col4" ><44.0</td>
+      <td id="T_7bb5b_row11_col5" class="data row11 col5" >-0.598058</td>
+      <td id="T_7bb5b_row11_col6" class="data row11 col6" >324.579816</td>
+      <td id="T_7bb5b_row11_col7" class="data row11 col7" >1.000000</td>
+      <td id="T_7bb5b_row11_col8" class="data row11 col8" >1</td>
+      <td id="T_7bb5b_row11_col9" class="data row11 col9" >CreditCards</td>
     </tr>
     <tr>
-      <td id="T_ac3fc_row12_col0" class="data row12 col0" >Customer.Age</td>
-      <td id="T_ac3fc_row12_col1" class="data row12 col1" >3</td>
-      <td id="T_ac3fc_row12_col2" class="data row12 col2" >44.000000</td>
-      <td id="T_ac3fc_row12_col3" class="data row12 col3" >56.000000</td>
-      <td id="T_ac3fc_row12_col4" class="data row12 col4" ><56.0</td>
-      <td id="T_ac3fc_row12_col5" class="data row12 col5" >0.996987</td>
-      <td id="T_ac3fc_row12_col6" class="data row12 col6" >82.989379</td>
-      <td id="T_ac3fc_row12_col7" class="data row12 col7" >1.000000</td>
-      <td id="T_ac3fc_row12_col8" class="data row12 col8" >1</td>
-      <td id="T_ac3fc_row12_col9" class="data row12 col9" >CreditCards</td>
+      <td id="T_7bb5b_row12_col0" class="data row12 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row12_col1" class="data row12 col1" >3</td>
+      <td id="T_7bb5b_row12_col2" class="data row12 col2" >44.000000</td>
+      <td id="T_7bb5b_row12_col3" class="data row12 col3" >56.000000</td>
+      <td id="T_7bb5b_row12_col4" class="data row12 col4" ><56.0</td>
+      <td id="T_7bb5b_row12_col5" class="data row12 col5" >0.996987</td>
+      <td id="T_7bb5b_row12_col6" class="data row12 col6" >82.989379</td>
+      <td id="T_7bb5b_row12_col7" class="data row12 col7" >1.000000</td>
+      <td id="T_7bb5b_row12_col8" class="data row12 col8" >1</td>
+      <td id="T_7bb5b_row12_col9" class="data row12 col9" >CreditCards</td>
     </tr>
     <tr>
-      <td id="T_ac3fc_row13_col0" class="data row13 col0" >Customer.Age</td>
-      <td id="T_ac3fc_row13_col1" class="data row13 col1" >4</td>
-      <td id="T_ac3fc_row13_col2" class="data row13 col2" >56.000000</td>
-      <td id="T_ac3fc_row13_col3" class="data row13 col3" >68.000000</td>
-      <td id="T_ac3fc_row13_col4" class="data row13 col4" ><68.0</td>
-      <td id="T_ac3fc_row13_col5" class="data row13 col5" >1.007692</td>
-      <td id="T_ac3fc_row13_col6" class="data row13 col6" >81.367929</td>
-      <td id="T_ac3fc_row13_col7" class="data row13 col7" >1.000000</td>
-      <td id="T_ac3fc_row13_col8" class="data row13 col8" >1</td>
-      <td id="T_ac3fc_row13_col9" class="data row13 col9" >CreditCards</td>
+      <td id="T_7bb5b_row13_col0" class="data row13 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row13_col1" class="data row13 col1" >4</td>
+      <td id="T_7bb5b_row13_col2" class="data row13 col2" >56.000000</td>
+      <td id="T_7bb5b_row13_col3" class="data row13 col3" >68.000000</td>
+      <td id="T_7bb5b_row13_col4" class="data row13 col4" ><68.0</td>
+      <td id="T_7bb5b_row13_col5" class="data row13 col5" >1.007692</td>
+      <td id="T_7bb5b_row13_col6" class="data row13 col6" >81.367929</td>
+      <td id="T_7bb5b_row13_col7" class="data row13 col7" >1.000000</td>
+      <td id="T_7bb5b_row13_col8" class="data row13 col8" >1</td>
+      <td id="T_7bb5b_row13_col9" class="data row13 col9" >CreditCards</td>
     </tr>
     <tr>
-      <td id="T_ac3fc_row14_col0" class="data row14 col0" >Customer.Age</td>
-      <td id="T_ac3fc_row14_col1" class="data row14 col1" >5</td>
-      <td id="T_ac3fc_row14_col2" class="data row14 col2" >68.000000</td>
-      <td id="T_ac3fc_row14_col3" class="data row14 col3" >80.000000</td>
-      <td id="T_ac3fc_row14_col4" class="data row14 col4" ><80.0</td>
-      <td id="T_ac3fc_row14_col5" class="data row14 col5" >1.007692</td>
-      <td id="T_ac3fc_row14_col6" class="data row14 col6" >67.806607</td>
-      <td id="T_ac3fc_row14_col7" class="data row14 col7" >0.833333</td>
-      <td id="T_ac3fc_row14_col8" class="data row14 col8" >1</td>
-      <td id="T_ac3fc_row14_col9" class="data row14 col9" >CreditCards</td>
+      <td id="T_7bb5b_row14_col0" class="data row14 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row14_col1" class="data row14 col1" >5</td>
+      <td id="T_7bb5b_row14_col2" class="data row14 col2" >68.000000</td>
+      <td id="T_7bb5b_row14_col3" class="data row14 col3" >80.000000</td>
+      <td id="T_7bb5b_row14_col4" class="data row14 col4" ><80.0</td>
+      <td id="T_7bb5b_row14_col5" class="data row14 col5" >1.007692</td>
+      <td id="T_7bb5b_row14_col6" class="data row14 col6" >67.806607</td>
+      <td id="T_7bb5b_row14_col7" class="data row14 col7" >0.833333</td>
+      <td id="T_7bb5b_row14_col8" class="data row14 col8" >1</td>
+      <td id="T_7bb5b_row14_col9" class="data row14 col9" >CreditCards</td>
     </tr>
     <tr>
-      <td id="T_ac3fc_row15_col0" class="data row15 col0" >Customer.Age</td>
-      <td id="T_ac3fc_row15_col1" class="data row15 col1" >1</td>
-      <td id="T_ac3fc_row15_col2" class="data row15 col2" >20.000000</td>
-      <td id="T_ac3fc_row15_col3" class="data row15 col3" >32.000000</td>
-      <td id="T_ac3fc_row15_col4" class="data row15 col4" ><32.0</td>
-      <td id="T_ac3fc_row15_col5" class="data row15 col5" >0.155355</td>
-      <td id="T_ac3fc_row15_col6" class="data row15 col6" >1749.921628</td>
-      <td id="T_ac3fc_row15_col7" class="data row15 col7" >2.000000</td>
-      <td id="T_ac3fc_row15_col8" class="data row15 col8" >2</td>
-      <td id="T_ac3fc_row15_col9" class="data row15 col9" >Bundles</td>
+      <td id="T_7bb5b_row15_col0" class="data row15 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row15_col1" class="data row15 col1" >1</td>
+      <td id="T_7bb5b_row15_col2" class="data row15 col2" >20.000000</td>
+      <td id="T_7bb5b_row15_col3" class="data row15 col3" >32.000000</td>
+      <td id="T_7bb5b_row15_col4" class="data row15 col4" ><32.0</td>
+      <td id="T_7bb5b_row15_col5" class="data row15 col5" >0.155355</td>
+      <td id="T_7bb5b_row15_col6" class="data row15 col6" >1749.921628</td>
+      <td id="T_7bb5b_row15_col7" class="data row15 col7" >2.000000</td>
+      <td id="T_7bb5b_row15_col8" class="data row15 col8" >2</td>
+      <td id="T_7bb5b_row15_col9" class="data row15 col9" >Bundles</td>
     </tr>
     <tr>
-      <td id="T_ac3fc_row16_col0" class="data row16 col0" >Customer.Age</td>
-      <td id="T_ac3fc_row16_col1" class="data row16 col1" >2</td>
-      <td id="T_ac3fc_row16_col2" class="data row16 col2" >32.000000</td>
-      <td id="T_ac3fc_row16_col3" class="data row16 col3" >44.000000</td>
-      <td id="T_ac3fc_row16_col4" class="data row16 col4" ><44.0</td>
-      <td id="T_ac3fc_row16_col5" class="data row16 col5" >-0.124786</td>
-      <td id="T_ac3fc_row16_col6" class="data row16 col6" >3979.820578</td>
-      <td id="T_ac3fc_row16_col7" class="data row16 col7" >2.000000</td>
-      <td id="T_ac3fc_row16_col8" class="data row16 col8" >2</td>
-      <td id="T_ac3fc_row16_col9" class="data row16 col9" >Bundles</td>
+      <td id="T_7bb5b_row16_col0" class="data row16 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row16_col1" class="data row16 col1" >2</td>
+      <td id="T_7bb5b_row16_col2" class="data row16 col2" >32.000000</td>
+      <td id="T_7bb5b_row16_col3" class="data row16 col3" >44.000000</td>
+      <td id="T_7bb5b_row16_col4" class="data row16 col4" ><44.0</td>
+      <td id="T_7bb5b_row16_col5" class="data row16 col5" >-0.124786</td>
+      <td id="T_7bb5b_row16_col6" class="data row16 col6" >3979.820578</td>
+      <td id="T_7bb5b_row16_col7" class="data row16 col7" >2.000000</td>
+      <td id="T_7bb5b_row16_col8" class="data row16 col8" >2</td>
+      <td id="T_7bb5b_row16_col9" class="data row16 col9" >Bundles</td>
     </tr>
     <tr>
-      <td id="T_ac3fc_row17_col0" class="data row17 col0" >Customer.Age</td>
-      <td id="T_ac3fc_row17_col1" class="data row17 col1" >3</td>
-      <td id="T_ac3fc_row17_col2" class="data row17 col2" >44.000000</td>
-      <td id="T_ac3fc_row17_col3" class="data row17 col3" >56.000000</td>
-      <td id="T_ac3fc_row17_col4" class="data row17 col4" ><56.0</td>
-      <td id="T_ac3fc_row17_col5" class="data row17 col5" >0.244397</td>
-      <td id="T_ac3fc_row17_col6" class="data row17 col6" >2843.339271</td>
-      <td id="T_ac3fc_row17_col7" class="data row17 col7" >2.000000</td>
-      <td id="T_ac3fc_row17_col8" class="data row17 col8" >2</td>
-      <td id="T_ac3fc_row17_col9" class="data row17 col9" >Bundles</td>
+      <td id="T_7bb5b_row17_col0" class="data row17 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row17_col1" class="data row17 col1" >3</td>
+      <td id="T_7bb5b_row17_col2" class="data row17 col2" >44.000000</td>
+      <td id="T_7bb5b_row17_col3" class="data row17 col3" >56.000000</td>
+      <td id="T_7bb5b_row17_col4" class="data row17 col4" ><56.0</td>
+      <td id="T_7bb5b_row17_col5" class="data row17 col5" >0.244397</td>
+      <td id="T_7bb5b_row17_col6" class="data row17 col6" >2843.339271</td>
+      <td id="T_7bb5b_row17_col7" class="data row17 col7" >2.000000</td>
+      <td id="T_7bb5b_row17_col8" class="data row17 col8" >2</td>
+      <td id="T_7bb5b_row17_col9" class="data row17 col9" >Bundles</td>
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-      <td id="T_ac3fc_row18_col0" class="data row18 col0" >Customer.Age</td>
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-      <td id="T_ac3fc_row18_col7" class="data row18 col7" >2.000000</td>
-      <td id="T_ac3fc_row18_col8" class="data row18 col8" >2</td>
-      <td id="T_ac3fc_row18_col9" class="data row18 col9" >Bundles</td>
+      <td id="T_7bb5b_row18_col0" class="data row18 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row18_col1" class="data row18 col1" >4</td>
+      <td id="T_7bb5b_row18_col2" class="data row18 col2" >56.000000</td>
+      <td id="T_7bb5b_row18_col3" class="data row18 col3" >68.000000</td>
+      <td id="T_7bb5b_row18_col4" class="data row18 col4" ><68.0</td>
+      <td id="T_7bb5b_row18_col5" class="data row18 col5" >-0.139278</td>
+      <td id="T_7bb5b_row18_col6" class="data row18 col6" >502.650345</td>
+      <td id="T_7bb5b_row18_col7" class="data row18 col7" >2.000000</td>
+      <td id="T_7bb5b_row18_col8" class="data row18 col8" >2</td>
+      <td id="T_7bb5b_row18_col9" class="data row18 col9" >Bundles</td>
     </tr>
     <tr>
-      <td id="T_ac3fc_row19_col0" class="data row19 col0" >Customer.Age</td>
-      <td id="T_ac3fc_row19_col1" class="data row19 col1" >5</td>
-      <td id="T_ac3fc_row19_col2" class="data row19 col2" >68.000000</td>
-      <td id="T_ac3fc_row19_col3" class="data row19 col3" >80.000000</td>
-      <td id="T_ac3fc_row19_col4" class="data row19 col4" ><80.0</td>
-      <td id="T_ac3fc_row19_col5" class="data row19 col5" >-0.209373</td>
-      <td id="T_ac3fc_row19_col6" class="data row19 col6" >331.583984</td>
-      <td id="T_ac3fc_row19_col7" class="data row19 col7" >1.666667</td>
-      <td id="T_ac3fc_row19_col8" class="data row19 col8" >2</td>
-      <td id="T_ac3fc_row19_col9" class="data row19 col9" >Bundles</td>
+      <td id="T_7bb5b_row19_col0" class="data row19 col0" >Customer.Age</td>
+      <td id="T_7bb5b_row19_col1" class="data row19 col1" >5</td>
+      <td id="T_7bb5b_row19_col2" class="data row19 col2" >68.000000</td>
+      <td id="T_7bb5b_row19_col3" class="data row19 col3" >80.000000</td>
+      <td id="T_7bb5b_row19_col4" class="data row19 col4" ><80.0</td>
+      <td id="T_7bb5b_row19_col5" class="data row19 col5" >-0.209373</td>
+      <td id="T_7bb5b_row19_col6" class="data row19 col6" >331.583984</td>
+      <td id="T_7bb5b_row19_col7" class="data row19 col7" >1.666667</td>
+      <td id="T_7bb5b_row19_col8" class="data row19 col8" >2</td>
+      <td id="T_7bb5b_row19_col9" class="data row19 col9" >Bundles</td>
     </tr>
   </tbody>
 </table></div>
@@ -992,9 +992,9 @@ <h2>Rolling up individual binning<a class="headerlink" href="#Rolling-up-individ
 <div class="prompt empty docutils container">
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@@ -387,19 +387,19 @@
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-     "shell.execute_reply": "2024-06-27T08:57:29.101327Z"
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    "execution_count": 7,
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-     "iopub.status.idle": "2024-06-27T08:57:29.268448Z",
-     "shell.execute_reply": "2024-06-27T08:57:29.267870Z"
+     "iopub.execute_input": "2024-06-28T08:59:05.614418Z",
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+     "iopub.status.idle": "2024-06-28T08:59:05.776750Z",
+     "shell.execute_reply": "2024-06-28T08:59:05.776178Z"
     }
    },
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        "                            \n",
-       "var gd = document.getElementById('b3ab1f39-7d2b-4604-be27-6cd736611fd0');\n",
+       "var gd = document.getElementById('3bcdc8c4-29ff-4a36-a387-38dcefc334f6');\n",
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        "        var display = window.getComputedStyle(gd).display;\n",
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@@ -509,10 +509,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2024-06-27T08:57:29.270263Z",
-     "iopub.status.idle": "2024-06-27T08:57:29.378132Z",
-     "shell.execute_reply": "2024-06-27T08:57:29.377690Z"
+     "iopub.execute_input": "2024-06-28T08:59:05.778878Z",
+     "iopub.status.busy": "2024-06-28T08:59:05.778608Z",
+     "iopub.status.idle": "2024-06-28T08:59:05.883136Z",
+     "shell.execute_reply": "2024-06-28T08:59:05.882567Z"
     }
    },
    "outputs": [
@@ -521,267 +521,267 @@
       "text/html": [
        "<style type=\"text/css\">\n",
        "</style>\n",
-       "<table id=\"T_ac3fc\">\n",
+       "<table id=\"T_7bb5b\">\n",
        "  <thead>\n",
        "    <tr>\n",
-       "      <th id=\"T_ac3fc_level0_col0\" class=\"col_heading level0 col0\" >PredictorName</th>\n",
-       "      <th id=\"T_ac3fc_level0_col1\" class=\"col_heading level0 col1\" >BinIndex</th>\n",
-       "      <th id=\"T_ac3fc_level0_col2\" class=\"col_heading level0 col2\" >BinLowerBound</th>\n",
-       "      <th id=\"T_ac3fc_level0_col3\" class=\"col_heading level0 col3\" >BinUpperBound</th>\n",
-       "      <th id=\"T_ac3fc_level0_col4\" class=\"col_heading level0 col4\" >BinSymbol</th>\n",
-       "      <th id=\"T_ac3fc_level0_col5\" class=\"col_heading level0 col5\" >Lift</th>\n",
-       "      <th id=\"T_ac3fc_level0_col6\" class=\"col_heading level0 col6\" >BinResponses</th>\n",
-       "      <th id=\"T_ac3fc_level0_col7\" class=\"col_heading level0 col7\" >BinCoverage</th>\n",
-       "      <th id=\"T_ac3fc_level0_col8\" class=\"col_heading level0 col8\" >Models</th>\n",
-       "      <th id=\"T_ac3fc_level0_col9\" class=\"col_heading level0 col9\" >Group</th>\n",
+       "      <th id=\"T_7bb5b_level0_col0\" class=\"col_heading level0 col0\" >PredictorName</th>\n",
+       "      <th id=\"T_7bb5b_level0_col1\" class=\"col_heading level0 col1\" >BinIndex</th>\n",
+       "      <th id=\"T_7bb5b_level0_col2\" class=\"col_heading level0 col2\" >BinLowerBound</th>\n",
+       "      <th id=\"T_7bb5b_level0_col3\" class=\"col_heading level0 col3\" >BinUpperBound</th>\n",
+       "      <th id=\"T_7bb5b_level0_col4\" class=\"col_heading level0 col4\" >BinSymbol</th>\n",
+       "      <th id=\"T_7bb5b_level0_col5\" class=\"col_heading level0 col5\" >Lift</th>\n",
+       "      <th id=\"T_7bb5b_level0_col6\" class=\"col_heading level0 col6\" >BinResponses</th>\n",
+       "      <th id=\"T_7bb5b_level0_col7\" class=\"col_heading level0 col7\" >BinCoverage</th>\n",
+       "      <th id=\"T_7bb5b_level0_col8\" class=\"col_heading level0 col8\" >Models</th>\n",
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        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row0_col0\" class=\"data row0 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row0_col1\" class=\"data row0 col1\" >1</td>\n",
-       "      <td id=\"T_ac3fc_row0_col2\" class=\"data row0 col2\" >20.000000</td>\n",
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-       "      <td id=\"T_ac3fc_row0_col8\" class=\"data row0 col8\" >10</td>\n",
-       "      <td id=\"T_ac3fc_row0_col9\" class=\"data row0 col9\" >AutoLoans</td>\n",
+       "      <td id=\"T_7bb5b_row0_col0\" class=\"data row0 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row0_col1\" class=\"data row0 col1\" >1</td>\n",
+       "      <td id=\"T_7bb5b_row0_col2\" class=\"data row0 col2\" >20.000000</td>\n",
+       "      <td id=\"T_7bb5b_row0_col3\" class=\"data row0 col3\" >32.000000</td>\n",
+       "      <td id=\"T_7bb5b_row0_col4\" class=\"data row0 col4\" ><32.0</td>\n",
+       "      <td id=\"T_7bb5b_row0_col5\" class=\"data row0 col5\" >0.229643</td>\n",
+       "      <td id=\"T_7bb5b_row0_col6\" class=\"data row0 col6\" >4398.940698</td>\n",
+       "      <td id=\"T_7bb5b_row0_col7\" class=\"data row0 col7\" >10.000000</td>\n",
+       "      <td id=\"T_7bb5b_row0_col8\" class=\"data row0 col8\" >10</td>\n",
+       "      <td id=\"T_7bb5b_row0_col9\" class=\"data row0 col9\" >AutoLoans</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row1_col0\" class=\"data row1 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row1_col1\" class=\"data row1 col1\" >2</td>\n",
-       "      <td id=\"T_ac3fc_row1_col2\" class=\"data row1 col2\" >32.000000</td>\n",
-       "      <td id=\"T_ac3fc_row1_col3\" class=\"data row1 col3\" >44.000000</td>\n",
-       "      <td id=\"T_ac3fc_row1_col4\" class=\"data row1 col4\" ><44.0</td>\n",
-       "      <td id=\"T_ac3fc_row1_col5\" class=\"data row1 col5\" >-0.261505</td>\n",
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-       "      <td id=\"T_ac3fc_row1_col7\" class=\"data row1 col7\" >10.000000</td>\n",
-       "      <td id=\"T_ac3fc_row1_col8\" class=\"data row1 col8\" >10</td>\n",
-       "      <td id=\"T_ac3fc_row1_col9\" class=\"data row1 col9\" >AutoLoans</td>\n",
+       "      <td id=\"T_7bb5b_row1_col0\" class=\"data row1 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row1_col1\" class=\"data row1 col1\" >2</td>\n",
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        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row2_col0\" class=\"data row2 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row2_col1\" class=\"data row2 col1\" >3</td>\n",
-       "      <td id=\"T_ac3fc_row2_col2\" class=\"data row2 col2\" >44.000000</td>\n",
-       "      <td id=\"T_ac3fc_row2_col3\" class=\"data row2 col3\" >56.000000</td>\n",
-       "      <td id=\"T_ac3fc_row2_col4\" class=\"data row2 col4\" ><56.0</td>\n",
-       "      <td id=\"T_ac3fc_row2_col5\" class=\"data row2 col5\" >0.281045</td>\n",
-       "      <td id=\"T_ac3fc_row2_col6\" class=\"data row2 col6\" >4540.748856</td>\n",
-       "      <td id=\"T_ac3fc_row2_col7\" class=\"data row2 col7\" >10.000000</td>\n",
-       "      <td id=\"T_ac3fc_row2_col8\" class=\"data row2 col8\" >10</td>\n",
-       "      <td id=\"T_ac3fc_row2_col9\" class=\"data row2 col9\" >AutoLoans</td>\n",
+       "      <td id=\"T_7bb5b_row2_col0\" class=\"data row2 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row2_col1\" class=\"data row2 col1\" >3</td>\n",
+       "      <td id=\"T_7bb5b_row2_col2\" class=\"data row2 col2\" >44.000000</td>\n",
+       "      <td id=\"T_7bb5b_row2_col3\" class=\"data row2 col3\" >56.000000</td>\n",
+       "      <td id=\"T_7bb5b_row2_col4\" class=\"data row2 col4\" ><56.0</td>\n",
+       "      <td id=\"T_7bb5b_row2_col5\" class=\"data row2 col5\" >0.281045</td>\n",
+       "      <td id=\"T_7bb5b_row2_col6\" class=\"data row2 col6\" >4540.748856</td>\n",
+       "      <td id=\"T_7bb5b_row2_col7\" class=\"data row2 col7\" >10.000000</td>\n",
+       "      <td id=\"T_7bb5b_row2_col8\" class=\"data row2 col8\" >10</td>\n",
+       "      <td id=\"T_7bb5b_row2_col9\" class=\"data row2 col9\" >AutoLoans</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row3_col0\" class=\"data row3 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row3_col1\" class=\"data row3 col1\" >4</td>\n",
-       "      <td id=\"T_ac3fc_row3_col2\" class=\"data row3 col2\" >56.000000</td>\n",
-       "      <td id=\"T_ac3fc_row3_col3\" class=\"data row3 col3\" >68.000000</td>\n",
-       "      <td id=\"T_ac3fc_row3_col4\" class=\"data row3 col4\" ><68.0</td>\n",
-       "      <td id=\"T_ac3fc_row3_col5\" class=\"data row3 col5\" >0.501178</td>\n",
-       "      <td id=\"T_ac3fc_row3_col6\" class=\"data row3 col6\" >1272.362768</td>\n",
-       "      <td id=\"T_ac3fc_row3_col7\" class=\"data row3 col7\" >10.000000</td>\n",
-       "      <td id=\"T_ac3fc_row3_col8\" class=\"data row3 col8\" >10</td>\n",
-       "      <td id=\"T_ac3fc_row3_col9\" class=\"data row3 col9\" >AutoLoans</td>\n",
+       "      <td id=\"T_7bb5b_row3_col0\" class=\"data row3 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row3_col1\" class=\"data row3 col1\" >4</td>\n",
+       "      <td id=\"T_7bb5b_row3_col2\" class=\"data row3 col2\" >56.000000</td>\n",
+       "      <td id=\"T_7bb5b_row3_col3\" class=\"data row3 col3\" >68.000000</td>\n",
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+       "      <td id=\"T_7bb5b_row3_col9\" class=\"data row3 col9\" >AutoLoans</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row4_col0\" class=\"data row4 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row4_col1\" class=\"data row4 col1\" >5</td>\n",
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-       "      <td id=\"T_ac3fc_row4_col3\" class=\"data row4 col3\" >80.000000</td>\n",
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-       "      <td id=\"T_ac3fc_row4_col5\" class=\"data row4 col5\" >0.510309</td>\n",
-       "      <td id=\"T_ac3fc_row4_col6\" class=\"data row4 col6\" >965.237926</td>\n",
-       "      <td id=\"T_ac3fc_row4_col7\" class=\"data row4 col7\" >8.166667</td>\n",
-       "      <td id=\"T_ac3fc_row4_col8\" class=\"data row4 col8\" >10</td>\n",
-       "      <td id=\"T_ac3fc_row4_col9\" class=\"data row4 col9\" >AutoLoans</td>\n",
+       "      <td id=\"T_7bb5b_row4_col0\" class=\"data row4 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row4_col1\" class=\"data row4 col1\" >5</td>\n",
+       "      <td id=\"T_7bb5b_row4_col2\" class=\"data row4 col2\" >68.000000</td>\n",
+       "      <td id=\"T_7bb5b_row4_col3\" class=\"data row4 col3\" >80.000000</td>\n",
+       "      <td id=\"T_7bb5b_row4_col4\" class=\"data row4 col4\" ><80.0</td>\n",
+       "      <td id=\"T_7bb5b_row4_col5\" class=\"data row4 col5\" >0.510309</td>\n",
+       "      <td id=\"T_7bb5b_row4_col6\" class=\"data row4 col6\" >965.237926</td>\n",
+       "      <td id=\"T_7bb5b_row4_col7\" class=\"data row4 col7\" >8.166667</td>\n",
+       "      <td id=\"T_7bb5b_row4_col8\" class=\"data row4 col8\" >10</td>\n",
+       "      <td id=\"T_7bb5b_row4_col9\" class=\"data row4 col9\" >AutoLoans</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row5_col0\" class=\"data row5 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row5_col1\" class=\"data row5 col1\" >1</td>\n",
-       "      <td id=\"T_ac3fc_row5_col2\" class=\"data row5 col2\" >20.000000</td>\n",
-       "      <td id=\"T_ac3fc_row5_col3\" class=\"data row5 col3\" >32.000000</td>\n",
-       "      <td id=\"T_ac3fc_row5_col4\" class=\"data row5 col4\" ><32.0</td>\n",
-       "      <td id=\"T_ac3fc_row5_col5\" class=\"data row5 col5\" >0.110590</td>\n",
-       "      <td id=\"T_ac3fc_row5_col6\" class=\"data row5 col6\" >995.724951</td>\n",
-       "      <td id=\"T_ac3fc_row5_col7\" class=\"data row5 col7\" >7.000000</td>\n",
-       "      <td id=\"T_ac3fc_row5_col8\" class=\"data row5 col8\" >7</td>\n",
-       "      <td id=\"T_ac3fc_row5_col9\" class=\"data row5 col9\" >HomeLoans</td>\n",
+       "      <td id=\"T_7bb5b_row5_col0\" class=\"data row5 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row5_col1\" class=\"data row5 col1\" >1</td>\n",
+       "      <td id=\"T_7bb5b_row5_col2\" class=\"data row5 col2\" >20.000000</td>\n",
+       "      <td id=\"T_7bb5b_row5_col3\" class=\"data row5 col3\" >32.000000</td>\n",
+       "      <td id=\"T_7bb5b_row5_col4\" class=\"data row5 col4\" ><32.0</td>\n",
+       "      <td id=\"T_7bb5b_row5_col5\" class=\"data row5 col5\" >0.110590</td>\n",
+       "      <td id=\"T_7bb5b_row5_col6\" class=\"data row5 col6\" >995.724951</td>\n",
+       "      <td id=\"T_7bb5b_row5_col7\" class=\"data row5 col7\" >7.000000</td>\n",
+       "      <td id=\"T_7bb5b_row5_col8\" class=\"data row5 col8\" >7</td>\n",
+       "      <td id=\"T_7bb5b_row5_col9\" class=\"data row5 col9\" >HomeLoans</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row6_col0\" class=\"data row6 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row6_col1\" class=\"data row6 col1\" >2</td>\n",
-       "      <td id=\"T_ac3fc_row6_col2\" class=\"data row6 col2\" >32.000000</td>\n",
-       "      <td id=\"T_ac3fc_row6_col3\" class=\"data row6 col3\" >44.000000</td>\n",
-       "      <td id=\"T_ac3fc_row6_col4\" class=\"data row6 col4\" ><44.0</td>\n",
-       "      <td id=\"T_ac3fc_row6_col5\" class=\"data row6 col5\" >-0.078623</td>\n",
-       "      <td id=\"T_ac3fc_row6_col6\" class=\"data row6 col6\" >1975.071325</td>\n",
-       "      <td id=\"T_ac3fc_row6_col7\" class=\"data row6 col7\" >7.000000</td>\n",
-       "      <td id=\"T_ac3fc_row6_col8\" class=\"data row6 col8\" >7</td>\n",
-       "      <td id=\"T_ac3fc_row6_col9\" class=\"data row6 col9\" >HomeLoans</td>\n",
+       "      <td id=\"T_7bb5b_row6_col0\" class=\"data row6 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row6_col1\" class=\"data row6 col1\" >2</td>\n",
+       "      <td id=\"T_7bb5b_row6_col2\" class=\"data row6 col2\" >32.000000</td>\n",
+       "      <td id=\"T_7bb5b_row6_col3\" class=\"data row6 col3\" >44.000000</td>\n",
+       "      <td id=\"T_7bb5b_row6_col4\" class=\"data row6 col4\" ><44.0</td>\n",
+       "      <td id=\"T_7bb5b_row6_col5\" class=\"data row6 col5\" >-0.078623</td>\n",
+       "      <td id=\"T_7bb5b_row6_col6\" class=\"data row6 col6\" >1975.071325</td>\n",
+       "      <td id=\"T_7bb5b_row6_col7\" class=\"data row6 col7\" >7.000000</td>\n",
+       "      <td id=\"T_7bb5b_row6_col8\" class=\"data row6 col8\" >7</td>\n",
+       "      <td id=\"T_7bb5b_row6_col9\" class=\"data row6 col9\" >HomeLoans</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row7_col0\" class=\"data row7 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row7_col1\" class=\"data row7 col1\" >3</td>\n",
-       "      <td id=\"T_ac3fc_row7_col2\" class=\"data row7 col2\" >44.000000</td>\n",
-       "      <td id=\"T_ac3fc_row7_col3\" class=\"data row7 col3\" >56.000000</td>\n",
-       "      <td id=\"T_ac3fc_row7_col4\" class=\"data row7 col4\" ><56.0</td>\n",
-       "      <td id=\"T_ac3fc_row7_col5\" class=\"data row7 col5\" >0.047313</td>\n",
-       "      <td id=\"T_ac3fc_row7_col6\" class=\"data row7 col6\" >1350.110244</td>\n",
-       "      <td id=\"T_ac3fc_row7_col7\" class=\"data row7 col7\" >7.000000</td>\n",
-       "      <td id=\"T_ac3fc_row7_col8\" class=\"data row7 col8\" >7</td>\n",
-       "      <td id=\"T_ac3fc_row7_col9\" class=\"data row7 col9\" >HomeLoans</td>\n",
+       "      <td id=\"T_7bb5b_row7_col0\" class=\"data row7 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row7_col1\" class=\"data row7 col1\" >3</td>\n",
+       "      <td id=\"T_7bb5b_row7_col2\" class=\"data row7 col2\" >44.000000</td>\n",
+       "      <td id=\"T_7bb5b_row7_col3\" class=\"data row7 col3\" >56.000000</td>\n",
+       "      <td id=\"T_7bb5b_row7_col4\" class=\"data row7 col4\" ><56.0</td>\n",
+       "      <td id=\"T_7bb5b_row7_col5\" class=\"data row7 col5\" >0.047313</td>\n",
+       "      <td id=\"T_7bb5b_row7_col6\" class=\"data row7 col6\" >1350.110244</td>\n",
+       "      <td id=\"T_7bb5b_row7_col7\" class=\"data row7 col7\" >7.000000</td>\n",
+       "      <td id=\"T_7bb5b_row7_col8\" class=\"data row7 col8\" >7</td>\n",
+       "      <td id=\"T_7bb5b_row7_col9\" class=\"data row7 col9\" >HomeLoans</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row8_col0\" class=\"data row8 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row8_col1\" class=\"data row8 col1\" >4</td>\n",
-       "      <td id=\"T_ac3fc_row8_col2\" class=\"data row8 col2\" >56.000000</td>\n",
-       "      <td id=\"T_ac3fc_row8_col3\" class=\"data row8 col3\" >68.000000</td>\n",
-       "      <td id=\"T_ac3fc_row8_col4\" class=\"data row8 col4\" ><68.0</td>\n",
-       "      <td id=\"T_ac3fc_row8_col5\" class=\"data row8 col5\" >0.145987</td>\n",
-       "      <td id=\"T_ac3fc_row8_col6\" class=\"data row8 col6\" >251.370112</td>\n",
-       "      <td id=\"T_ac3fc_row8_col7\" class=\"data row8 col7\" >7.000000</td>\n",
-       "      <td id=\"T_ac3fc_row8_col8\" class=\"data row8 col8\" >7</td>\n",
-       "      <td id=\"T_ac3fc_row8_col9\" class=\"data row8 col9\" >HomeLoans</td>\n",
+       "      <td id=\"T_7bb5b_row8_col0\" class=\"data row8 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row8_col1\" class=\"data row8 col1\" >4</td>\n",
+       "      <td id=\"T_7bb5b_row8_col2\" class=\"data row8 col2\" >56.000000</td>\n",
+       "      <td id=\"T_7bb5b_row8_col3\" class=\"data row8 col3\" >68.000000</td>\n",
+       "      <td id=\"T_7bb5b_row8_col4\" class=\"data row8 col4\" ><68.0</td>\n",
+       "      <td id=\"T_7bb5b_row8_col5\" class=\"data row8 col5\" >0.145987</td>\n",
+       "      <td id=\"T_7bb5b_row8_col6\" class=\"data row8 col6\" >251.370112</td>\n",
+       "      <td id=\"T_7bb5b_row8_col7\" class=\"data row8 col7\" >7.000000</td>\n",
+       "      <td id=\"T_7bb5b_row8_col8\" class=\"data row8 col8\" >7</td>\n",
+       "      <td id=\"T_7bb5b_row8_col9\" class=\"data row8 col9\" >HomeLoans</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row9_col0\" class=\"data row9 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row9_col1\" class=\"data row9 col1\" >5</td>\n",
-       "      <td id=\"T_ac3fc_row9_col2\" class=\"data row9 col2\" >68.000000</td>\n",
-       "      <td id=\"T_ac3fc_row9_col3\" class=\"data row9 col3\" >80.000000</td>\n",
-       "      <td id=\"T_ac3fc_row9_col4\" class=\"data row9 col4\" ><80.0</td>\n",
-       "      <td id=\"T_ac3fc_row9_col5\" class=\"data row9 col5\" >0.167526</td>\n",
-       "      <td id=\"T_ac3fc_row9_col6\" class=\"data row9 col6\" >209.475093</td>\n",
-       "      <td id=\"T_ac3fc_row9_col7\" class=\"data row9 col7\" >5.083333</td>\n",
-       "      <td id=\"T_ac3fc_row9_col8\" class=\"data row9 col8\" >7</td>\n",
-       "      <td id=\"T_ac3fc_row9_col9\" class=\"data row9 col9\" >HomeLoans</td>\n",
+       "      <td id=\"T_7bb5b_row9_col0\" class=\"data row9 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row9_col1\" class=\"data row9 col1\" >5</td>\n",
+       "      <td id=\"T_7bb5b_row9_col2\" class=\"data row9 col2\" >68.000000</td>\n",
+       "      <td id=\"T_7bb5b_row9_col3\" class=\"data row9 col3\" >80.000000</td>\n",
+       "      <td id=\"T_7bb5b_row9_col4\" class=\"data row9 col4\" ><80.0</td>\n",
+       "      <td id=\"T_7bb5b_row9_col5\" class=\"data row9 col5\" >0.167526</td>\n",
+       "      <td id=\"T_7bb5b_row9_col6\" class=\"data row9 col6\" >209.475093</td>\n",
+       "      <td id=\"T_7bb5b_row9_col7\" class=\"data row9 col7\" >5.083333</td>\n",
+       "      <td id=\"T_7bb5b_row9_col8\" class=\"data row9 col8\" >7</td>\n",
+       "      <td id=\"T_7bb5b_row9_col9\" class=\"data row9 col9\" >HomeLoans</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row10_col0\" class=\"data row10 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row10_col1\" class=\"data row10 col1\" >1</td>\n",
-       "      <td id=\"T_ac3fc_row10_col2\" class=\"data row10 col2\" >20.000000</td>\n",
-       "      <td id=\"T_ac3fc_row10_col3\" class=\"data row10 col3\" >32.000000</td>\n",
-       "      <td id=\"T_ac3fc_row10_col4\" class=\"data row10 col4\" ><32.0</td>\n",
-       "      <td id=\"T_ac3fc_row10_col5\" class=\"data row10 col5\" >0.038873</td>\n",
-       "      <td id=\"T_ac3fc_row10_col6\" class=\"data row10 col6\" >247.676726</td>\n",
-       "      <td id=\"T_ac3fc_row10_col7\" class=\"data row10 col7\" >1.000000</td>\n",
-       "      <td id=\"T_ac3fc_row10_col8\" class=\"data row10 col8\" >1</td>\n",
-       "      <td id=\"T_ac3fc_row10_col9\" class=\"data row10 col9\" >CreditCards</td>\n",
+       "      <td id=\"T_7bb5b_row10_col0\" class=\"data row10 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row10_col1\" class=\"data row10 col1\" >1</td>\n",
+       "      <td id=\"T_7bb5b_row10_col2\" class=\"data row10 col2\" >20.000000</td>\n",
+       "      <td id=\"T_7bb5b_row10_col3\" class=\"data row10 col3\" >32.000000</td>\n",
+       "      <td id=\"T_7bb5b_row10_col4\" class=\"data row10 col4\" ><32.0</td>\n",
+       "      <td id=\"T_7bb5b_row10_col5\" class=\"data row10 col5\" >0.038873</td>\n",
+       "      <td id=\"T_7bb5b_row10_col6\" class=\"data row10 col6\" >247.676726</td>\n",
+       "      <td id=\"T_7bb5b_row10_col7\" class=\"data row10 col7\" >1.000000</td>\n",
+       "      <td id=\"T_7bb5b_row10_col8\" class=\"data row10 col8\" >1</td>\n",
+       "      <td id=\"T_7bb5b_row10_col9\" class=\"data row10 col9\" >CreditCards</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row11_col0\" class=\"data row11 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row11_col1\" class=\"data row11 col1\" >2</td>\n",
-       "      <td id=\"T_ac3fc_row11_col2\" class=\"data row11 col2\" >32.000000</td>\n",
-       "      <td id=\"T_ac3fc_row11_col3\" class=\"data row11 col3\" >44.000000</td>\n",
-       "      <td id=\"T_ac3fc_row11_col4\" class=\"data row11 col4\" ><44.0</td>\n",
-       "      <td id=\"T_ac3fc_row11_col5\" class=\"data row11 col5\" >-0.598058</td>\n",
-       "      <td id=\"T_ac3fc_row11_col6\" class=\"data row11 col6\" >324.579816</td>\n",
-       "      <td id=\"T_ac3fc_row11_col7\" class=\"data row11 col7\" >1.000000</td>\n",
-       "      <td id=\"T_ac3fc_row11_col8\" class=\"data row11 col8\" >1</td>\n",
-       "      <td id=\"T_ac3fc_row11_col9\" class=\"data row11 col9\" >CreditCards</td>\n",
+       "      <td id=\"T_7bb5b_row11_col0\" class=\"data row11 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row11_col1\" class=\"data row11 col1\" >2</td>\n",
+       "      <td id=\"T_7bb5b_row11_col2\" class=\"data row11 col2\" >32.000000</td>\n",
+       "      <td id=\"T_7bb5b_row11_col3\" class=\"data row11 col3\" >44.000000</td>\n",
+       "      <td id=\"T_7bb5b_row11_col4\" class=\"data row11 col4\" ><44.0</td>\n",
+       "      <td id=\"T_7bb5b_row11_col5\" class=\"data row11 col5\" >-0.598058</td>\n",
+       "      <td id=\"T_7bb5b_row11_col6\" class=\"data row11 col6\" >324.579816</td>\n",
+       "      <td id=\"T_7bb5b_row11_col7\" class=\"data row11 col7\" >1.000000</td>\n",
+       "      <td id=\"T_7bb5b_row11_col8\" class=\"data row11 col8\" >1</td>\n",
+       "      <td id=\"T_7bb5b_row11_col9\" class=\"data row11 col9\" >CreditCards</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row12_col0\" class=\"data row12 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row12_col1\" class=\"data row12 col1\" >3</td>\n",
-       "      <td id=\"T_ac3fc_row12_col2\" class=\"data row12 col2\" >44.000000</td>\n",
-       "      <td id=\"T_ac3fc_row12_col3\" class=\"data row12 col3\" >56.000000</td>\n",
-       "      <td id=\"T_ac3fc_row12_col4\" class=\"data row12 col4\" ><56.0</td>\n",
-       "      <td id=\"T_ac3fc_row12_col5\" class=\"data row12 col5\" >0.996987</td>\n",
-       "      <td id=\"T_ac3fc_row12_col6\" class=\"data row12 col6\" >82.989379</td>\n",
-       "      <td id=\"T_ac3fc_row12_col7\" class=\"data row12 col7\" >1.000000</td>\n",
-       "      <td id=\"T_ac3fc_row12_col8\" class=\"data row12 col8\" >1</td>\n",
-       "      <td id=\"T_ac3fc_row12_col9\" class=\"data row12 col9\" >CreditCards</td>\n",
+       "      <td id=\"T_7bb5b_row12_col0\" class=\"data row12 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row12_col1\" class=\"data row12 col1\" >3</td>\n",
+       "      <td id=\"T_7bb5b_row12_col2\" class=\"data row12 col2\" >44.000000</td>\n",
+       "      <td id=\"T_7bb5b_row12_col3\" class=\"data row12 col3\" >56.000000</td>\n",
+       "      <td id=\"T_7bb5b_row12_col4\" class=\"data row12 col4\" ><56.0</td>\n",
+       "      <td id=\"T_7bb5b_row12_col5\" class=\"data row12 col5\" >0.996987</td>\n",
+       "      <td id=\"T_7bb5b_row12_col6\" class=\"data row12 col6\" >82.989379</td>\n",
+       "      <td id=\"T_7bb5b_row12_col7\" class=\"data row12 col7\" >1.000000</td>\n",
+       "      <td id=\"T_7bb5b_row12_col8\" class=\"data row12 col8\" >1</td>\n",
+       "      <td id=\"T_7bb5b_row12_col9\" class=\"data row12 col9\" >CreditCards</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row13_col0\" class=\"data row13 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row13_col1\" class=\"data row13 col1\" >4</td>\n",
-       "      <td id=\"T_ac3fc_row13_col2\" class=\"data row13 col2\" >56.000000</td>\n",
-       "      <td id=\"T_ac3fc_row13_col3\" class=\"data row13 col3\" >68.000000</td>\n",
-       "      <td id=\"T_ac3fc_row13_col4\" class=\"data row13 col4\" ><68.0</td>\n",
-       "      <td id=\"T_ac3fc_row13_col5\" class=\"data row13 col5\" >1.007692</td>\n",
-       "      <td id=\"T_ac3fc_row13_col6\" class=\"data row13 col6\" >81.367929</td>\n",
-       "      <td id=\"T_ac3fc_row13_col7\" class=\"data row13 col7\" >1.000000</td>\n",
-       "      <td id=\"T_ac3fc_row13_col8\" class=\"data row13 col8\" >1</td>\n",
-       "      <td id=\"T_ac3fc_row13_col9\" class=\"data row13 col9\" >CreditCards</td>\n",
+       "      <td id=\"T_7bb5b_row13_col0\" class=\"data row13 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row13_col1\" class=\"data row13 col1\" >4</td>\n",
+       "      <td id=\"T_7bb5b_row13_col2\" class=\"data row13 col2\" >56.000000</td>\n",
+       "      <td id=\"T_7bb5b_row13_col3\" class=\"data row13 col3\" >68.000000</td>\n",
+       "      <td id=\"T_7bb5b_row13_col4\" class=\"data row13 col4\" ><68.0</td>\n",
+       "      <td id=\"T_7bb5b_row13_col5\" class=\"data row13 col5\" >1.007692</td>\n",
+       "      <td id=\"T_7bb5b_row13_col6\" class=\"data row13 col6\" >81.367929</td>\n",
+       "      <td id=\"T_7bb5b_row13_col7\" class=\"data row13 col7\" >1.000000</td>\n",
+       "      <td id=\"T_7bb5b_row13_col8\" class=\"data row13 col8\" >1</td>\n",
+       "      <td id=\"T_7bb5b_row13_col9\" class=\"data row13 col9\" >CreditCards</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row14_col0\" class=\"data row14 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row14_col1\" class=\"data row14 col1\" >5</td>\n",
-       "      <td id=\"T_ac3fc_row14_col2\" class=\"data row14 col2\" >68.000000</td>\n",
-       "      <td id=\"T_ac3fc_row14_col3\" class=\"data row14 col3\" >80.000000</td>\n",
-       "      <td id=\"T_ac3fc_row14_col4\" class=\"data row14 col4\" ><80.0</td>\n",
-       "      <td id=\"T_ac3fc_row14_col5\" class=\"data row14 col5\" >1.007692</td>\n",
-       "      <td id=\"T_ac3fc_row14_col6\" class=\"data row14 col6\" >67.806607</td>\n",
-       "      <td id=\"T_ac3fc_row14_col7\" class=\"data row14 col7\" >0.833333</td>\n",
-       "      <td id=\"T_ac3fc_row14_col8\" class=\"data row14 col8\" >1</td>\n",
-       "      <td id=\"T_ac3fc_row14_col9\" class=\"data row14 col9\" >CreditCards</td>\n",
+       "      <td id=\"T_7bb5b_row14_col0\" class=\"data row14 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row14_col1\" class=\"data row14 col1\" >5</td>\n",
+       "      <td id=\"T_7bb5b_row14_col2\" class=\"data row14 col2\" >68.000000</td>\n",
+       "      <td id=\"T_7bb5b_row14_col3\" class=\"data row14 col3\" >80.000000</td>\n",
+       "      <td id=\"T_7bb5b_row14_col4\" class=\"data row14 col4\" ><80.0</td>\n",
+       "      <td id=\"T_7bb5b_row14_col5\" class=\"data row14 col5\" >1.007692</td>\n",
+       "      <td id=\"T_7bb5b_row14_col6\" class=\"data row14 col6\" >67.806607</td>\n",
+       "      <td id=\"T_7bb5b_row14_col7\" class=\"data row14 col7\" >0.833333</td>\n",
+       "      <td id=\"T_7bb5b_row14_col8\" class=\"data row14 col8\" >1</td>\n",
+       "      <td id=\"T_7bb5b_row14_col9\" class=\"data row14 col9\" >CreditCards</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row15_col0\" class=\"data row15 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row15_col1\" class=\"data row15 col1\" >1</td>\n",
-       "      <td id=\"T_ac3fc_row15_col2\" class=\"data row15 col2\" >20.000000</td>\n",
-       "      <td id=\"T_ac3fc_row15_col3\" class=\"data row15 col3\" >32.000000</td>\n",
-       "      <td id=\"T_ac3fc_row15_col4\" class=\"data row15 col4\" ><32.0</td>\n",
-       "      <td id=\"T_ac3fc_row15_col5\" class=\"data row15 col5\" >0.155355</td>\n",
-       "      <td id=\"T_ac3fc_row15_col6\" class=\"data row15 col6\" >1749.921628</td>\n",
-       "      <td id=\"T_ac3fc_row15_col7\" class=\"data row15 col7\" >2.000000</td>\n",
-       "      <td id=\"T_ac3fc_row15_col8\" class=\"data row15 col8\" >2</td>\n",
-       "      <td id=\"T_ac3fc_row15_col9\" class=\"data row15 col9\" >Bundles</td>\n",
+       "      <td id=\"T_7bb5b_row15_col0\" class=\"data row15 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row15_col1\" class=\"data row15 col1\" >1</td>\n",
+       "      <td id=\"T_7bb5b_row15_col2\" class=\"data row15 col2\" >20.000000</td>\n",
+       "      <td id=\"T_7bb5b_row15_col3\" class=\"data row15 col3\" >32.000000</td>\n",
+       "      <td id=\"T_7bb5b_row15_col4\" class=\"data row15 col4\" ><32.0</td>\n",
+       "      <td id=\"T_7bb5b_row15_col5\" class=\"data row15 col5\" >0.155355</td>\n",
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+       "      <td id=\"T_7bb5b_row15_col7\" class=\"data row15 col7\" >2.000000</td>\n",
+       "      <td id=\"T_7bb5b_row15_col8\" class=\"data row15 col8\" >2</td>\n",
+       "      <td id=\"T_7bb5b_row15_col9\" class=\"data row15 col9\" >Bundles</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row16_col0\" class=\"data row16 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row16_col1\" class=\"data row16 col1\" >2</td>\n",
-       "      <td id=\"T_ac3fc_row16_col2\" class=\"data row16 col2\" >32.000000</td>\n",
-       "      <td id=\"T_ac3fc_row16_col3\" class=\"data row16 col3\" >44.000000</td>\n",
-       "      <td id=\"T_ac3fc_row16_col4\" class=\"data row16 col4\" ><44.0</td>\n",
-       "      <td id=\"T_ac3fc_row16_col5\" class=\"data row16 col5\" >-0.124786</td>\n",
-       "      <td id=\"T_ac3fc_row16_col6\" class=\"data row16 col6\" >3979.820578</td>\n",
-       "      <td id=\"T_ac3fc_row16_col7\" class=\"data row16 col7\" >2.000000</td>\n",
-       "      <td id=\"T_ac3fc_row16_col8\" class=\"data row16 col8\" >2</td>\n",
-       "      <td id=\"T_ac3fc_row16_col9\" class=\"data row16 col9\" >Bundles</td>\n",
+       "      <td id=\"T_7bb5b_row16_col0\" class=\"data row16 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row16_col1\" class=\"data row16 col1\" >2</td>\n",
+       "      <td id=\"T_7bb5b_row16_col2\" class=\"data row16 col2\" >32.000000</td>\n",
+       "      <td id=\"T_7bb5b_row16_col3\" class=\"data row16 col3\" >44.000000</td>\n",
+       "      <td id=\"T_7bb5b_row16_col4\" class=\"data row16 col4\" ><44.0</td>\n",
+       "      <td id=\"T_7bb5b_row16_col5\" class=\"data row16 col5\" >-0.124786</td>\n",
+       "      <td id=\"T_7bb5b_row16_col6\" class=\"data row16 col6\" >3979.820578</td>\n",
+       "      <td id=\"T_7bb5b_row16_col7\" class=\"data row16 col7\" >2.000000</td>\n",
+       "      <td id=\"T_7bb5b_row16_col8\" class=\"data row16 col8\" >2</td>\n",
+       "      <td id=\"T_7bb5b_row16_col9\" class=\"data row16 col9\" >Bundles</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row17_col0\" class=\"data row17 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row17_col1\" class=\"data row17 col1\" >3</td>\n",
-       "      <td id=\"T_ac3fc_row17_col2\" class=\"data row17 col2\" >44.000000</td>\n",
-       "      <td id=\"T_ac3fc_row17_col3\" class=\"data row17 col3\" >56.000000</td>\n",
-       "      <td id=\"T_ac3fc_row17_col4\" class=\"data row17 col4\" ><56.0</td>\n",
-       "      <td id=\"T_ac3fc_row17_col5\" class=\"data row17 col5\" >0.244397</td>\n",
-       "      <td id=\"T_ac3fc_row17_col6\" class=\"data row17 col6\" >2843.339271</td>\n",
-       "      <td id=\"T_ac3fc_row17_col7\" class=\"data row17 col7\" >2.000000</td>\n",
-       "      <td id=\"T_ac3fc_row17_col8\" class=\"data row17 col8\" >2</td>\n",
-       "      <td id=\"T_ac3fc_row17_col9\" class=\"data row17 col9\" >Bundles</td>\n",
+       "      <td id=\"T_7bb5b_row17_col0\" class=\"data row17 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row17_col1\" class=\"data row17 col1\" >3</td>\n",
+       "      <td id=\"T_7bb5b_row17_col2\" class=\"data row17 col2\" >44.000000</td>\n",
+       "      <td id=\"T_7bb5b_row17_col3\" class=\"data row17 col3\" >56.000000</td>\n",
+       "      <td id=\"T_7bb5b_row17_col4\" class=\"data row17 col4\" ><56.0</td>\n",
+       "      <td id=\"T_7bb5b_row17_col5\" class=\"data row17 col5\" >0.244397</td>\n",
+       "      <td id=\"T_7bb5b_row17_col6\" class=\"data row17 col6\" >2843.339271</td>\n",
+       "      <td id=\"T_7bb5b_row17_col7\" class=\"data row17 col7\" >2.000000</td>\n",
+       "      <td id=\"T_7bb5b_row17_col8\" class=\"data row17 col8\" >2</td>\n",
+       "      <td id=\"T_7bb5b_row17_col9\" class=\"data row17 col9\" >Bundles</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row18_col0\" class=\"data row18 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row18_col1\" class=\"data row18 col1\" >4</td>\n",
-       "      <td id=\"T_ac3fc_row18_col2\" class=\"data row18 col2\" >56.000000</td>\n",
-       "      <td id=\"T_ac3fc_row18_col3\" class=\"data row18 col3\" >68.000000</td>\n",
-       "      <td id=\"T_ac3fc_row18_col4\" class=\"data row18 col4\" ><68.0</td>\n",
-       "      <td id=\"T_ac3fc_row18_col5\" class=\"data row18 col5\" >-0.139278</td>\n",
-       "      <td id=\"T_ac3fc_row18_col6\" class=\"data row18 col6\" >502.650345</td>\n",
-       "      <td id=\"T_ac3fc_row18_col7\" class=\"data row18 col7\" >2.000000</td>\n",
-       "      <td id=\"T_ac3fc_row18_col8\" class=\"data row18 col8\" >2</td>\n",
-       "      <td id=\"T_ac3fc_row18_col9\" class=\"data row18 col9\" >Bundles</td>\n",
+       "      <td id=\"T_7bb5b_row18_col0\" class=\"data row18 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row18_col1\" class=\"data row18 col1\" >4</td>\n",
+       "      <td id=\"T_7bb5b_row18_col2\" class=\"data row18 col2\" >56.000000</td>\n",
+       "      <td id=\"T_7bb5b_row18_col3\" class=\"data row18 col3\" >68.000000</td>\n",
+       "      <td id=\"T_7bb5b_row18_col4\" class=\"data row18 col4\" ><68.0</td>\n",
+       "      <td id=\"T_7bb5b_row18_col5\" class=\"data row18 col5\" >-0.139278</td>\n",
+       "      <td id=\"T_7bb5b_row18_col6\" class=\"data row18 col6\" >502.650345</td>\n",
+       "      <td id=\"T_7bb5b_row18_col7\" class=\"data row18 col7\" >2.000000</td>\n",
+       "      <td id=\"T_7bb5b_row18_col8\" class=\"data row18 col8\" >2</td>\n",
+       "      <td id=\"T_7bb5b_row18_col9\" class=\"data row18 col9\" >Bundles</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_ac3fc_row19_col0\" class=\"data row19 col0\" >Customer.Age</td>\n",
-       "      <td id=\"T_ac3fc_row19_col1\" class=\"data row19 col1\" >5</td>\n",
-       "      <td id=\"T_ac3fc_row19_col2\" class=\"data row19 col2\" >68.000000</td>\n",
-       "      <td id=\"T_ac3fc_row19_col3\" class=\"data row19 col3\" >80.000000</td>\n",
-       "      <td id=\"T_ac3fc_row19_col4\" class=\"data row19 col4\" ><80.0</td>\n",
-       "      <td id=\"T_ac3fc_row19_col5\" class=\"data row19 col5\" >-0.209373</td>\n",
-       "      <td id=\"T_ac3fc_row19_col6\" class=\"data row19 col6\" >331.583984</td>\n",
-       "      <td id=\"T_ac3fc_row19_col7\" class=\"data row19 col7\" >1.666667</td>\n",
-       "      <td id=\"T_ac3fc_row19_col8\" class=\"data row19 col8\" >2</td>\n",
-       "      <td id=\"T_ac3fc_row19_col9\" class=\"data row19 col9\" >Bundles</td>\n",
+       "      <td id=\"T_7bb5b_row19_col0\" class=\"data row19 col0\" >Customer.Age</td>\n",
+       "      <td id=\"T_7bb5b_row19_col1\" class=\"data row19 col1\" >5</td>\n",
+       "      <td id=\"T_7bb5b_row19_col2\" class=\"data row19 col2\" >68.000000</td>\n",
+       "      <td id=\"T_7bb5b_row19_col3\" class=\"data row19 col3\" >80.000000</td>\n",
+       "      <td id=\"T_7bb5b_row19_col4\" class=\"data row19 col4\" ><80.0</td>\n",
+       "      <td id=\"T_7bb5b_row19_col5\" class=\"data row19 col5\" >-0.209373</td>\n",
+       "      <td id=\"T_7bb5b_row19_col6\" class=\"data row19 col6\" >331.583984</td>\n",
+       "      <td id=\"T_7bb5b_row19_col7\" class=\"data row19 col7\" >1.666667</td>\n",
+       "      <td id=\"T_7bb5b_row19_col8\" class=\"data row19 col8\" >2</td>\n",
+       "      <td id=\"T_7bb5b_row19_col9\" class=\"data row19 col9\" >Bundles</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fd42cfa0a10>"
+       "<pandas.io.formats.style.Styler at 0x7f34a821e950>"
       ]
      },
      "execution_count": 8,
@@ -807,19 +807,19 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:29.380383Z",
-     "iopub.status.busy": "2024-06-27T08:57:29.380006Z",
-     "iopub.status.idle": "2024-06-27T08:57:29.509877Z",
-     "shell.execute_reply": "2024-06-27T08:57:29.509391Z"
+     "iopub.execute_input": "2024-06-28T08:59:05.885304Z",
+     "iopub.status.busy": "2024-06-28T08:59:05.884856Z",
+     "iopub.status.idle": "2024-06-28T08:59:06.014200Z",
+     "shell.execute_reply": "2024-06-28T08:59:06.013758Z"
     }
    },
    "outputs": [
     {
      "data": {
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+var gd = document.getElementById('4b0e35ce-a247-4f45-bc82-45045d61c103');
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@@ -770,20 +770,20 @@ <h3>Positive and Negative ratios<a class="headerlink" href="#Positive-and-Negati
 </div>
 <div class="output_area rendered_html docutils container">
 <style type="text/css">
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 </style>
-<table id="T_13005">
+<table id="T_63f48">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_13005_level0_col0" class="col_heading level0 col0" >Positives</th>
-      <th id="T_13005_level0_col1" class="col_heading level0 col1" >Negatives</th>
-      <th id="T_13005_level0_col2" class="col_heading level0 col2" >Responses %</th>
-      <th id="T_13005_level0_col3" class="col_heading level0 col3" >Positives %</th>
-      <th id="T_13005_level0_col4" class="col_heading level0 col4" >Negatives %</th>
-      <th id="T_13005_level0_col5" class="col_heading level0 col5" >Propensity</th>
+      <th id="T_63f48_level0_col0" class="col_heading level0 col0" >Positives</th>
+      <th id="T_63f48_level0_col1" class="col_heading level0 col1" >Negatives</th>
+      <th id="T_63f48_level0_col2" class="col_heading level0 col2" >Responses %</th>
+      <th id="T_63f48_level0_col3" class="col_heading level0 col3" >Positives %</th>
+      <th id="T_63f48_level0_col4" class="col_heading level0 col4" >Negatives %</th>
+      <th id="T_63f48_level0_col5" class="col_heading level0 col5" >Propensity</th>
     </tr>
     <tr>
       <th class="index_name level0" >Range/Symbol</th>
@@ -797,76 +797,76 @@ <h3>Positive and Negative ratios<a class="headerlink" href="#Positive-and-Negati
   </thead>
   <tbody>
     <tr>
-      <th id="T_13005_level0_row0" class="row_heading level0 row0" ><11684.56</th>
-      <td id="T_13005_row0_col0" class="data row0 col0" >13</td>
-      <td id="T_13005_row0_col1" class="data row0 col1" >423</td>
-      <td id="T_13005_row0_col2" class="data row0 col2" >26.65</td>
-      <td id="T_13005_row0_col3" class="data row0 col3" >6.31</td>
-      <td id="T_13005_row0_col4" class="data row0 col4" >29.58</td>
-      <td id="T_13005_row0_col5" class="data row0 col5" >0.0298</td>
-    </tr>
-    <tr>
-      <th id="T_13005_level0_row1" class="row_heading level0 row1" >[11684.56, 13732.56></th>
-      <td id="T_13005_row1_col0" class="data row1 col0" >24</td>
-      <td id="T_13005_row1_col1" class="data row1 col1" >178</td>
-      <td id="T_13005_row1_col2" class="data row1 col2" >12.35</td>
-      <td id="T_13005_row1_col3" class="data row1 col3" >11.65</td>
-      <td id="T_13005_row1_col4" class="data row1 col4" >12.45</td>
-      <td id="T_13005_row1_col5" class="data row1 col5" >0.1188</td>
-    </tr>
-    <tr>
-      <th id="T_13005_level0_row2" class="row_heading level0 row2" >[13732.56, 16845.52></th>
-      <td id="T_13005_row2_col0" class="data row2 col0" >17</td>
-      <td id="T_13005_row2_col1" class="data row2 col1" >250</td>
-      <td id="T_13005_row2_col2" class="data row2 col2" >16.32</td>
-      <td id="T_13005_row2_col3" class="data row2 col3" >8.25</td>
-      <td id="T_13005_row2_col4" class="data row2 col4" >17.48</td>
-      <td id="T_13005_row2_col5" class="data row2 col5" >0.0637</td>
-    </tr>
-    <tr>
-      <th id="T_13005_level0_row3" class="row_heading level0 row3" >[16845.52, 19139.28></th>
-      <td id="T_13005_row3_col0" class="data row3 col0" >51</td>
-      <td id="T_13005_row3_col1" class="data row3 col1" >179</td>
-      <td id="T_13005_row3_col2" class="data row3 col2" >14.06</td>
-      <td id="T_13005_row3_col3" class="data row3 col3" >24.76</td>
-      <td id="T_13005_row3_col4" class="data row3 col4" >12.52</td>
-      <td id="T_13005_row3_col5" class="data row3 col5" >0.2217</td>
-    </tr>
-    <tr>
-      <th id="T_13005_level0_row4" class="row_heading level0 row4" >[19139.28, 20286.16></th>
-      <td id="T_13005_row4_col0" class="data row4 col0" >7</td>
-      <td id="T_13005_row4_col1" class="data row4 col1" >83</td>
-      <td id="T_13005_row4_col2" class="data row4 col2" >5.50</td>
-      <td id="T_13005_row4_col3" class="data row4 col3" >3.40</td>
-      <td id="T_13005_row4_col4" class="data row4 col4" >5.80</td>
-      <td id="T_13005_row4_col5" class="data row4 col5" >0.0778</td>
-    </tr>
-    <tr>
-      <th id="T_13005_level0_row5" class="row_heading level0 row5" >[20286.16, 22743.76></th>
-      <td id="T_13005_row5_col0" class="data row5 col0" >53</td>
-      <td id="T_13005_row5_col1" class="data row5 col1" >169</td>
-      <td id="T_13005_row5_col2" class="data row5 col2" >13.57</td>
-      <td id="T_13005_row5_col3" class="data row5 col3" >25.73</td>
-      <td id="T_13005_row5_col4" class="data row5 col4" >11.82</td>
-      <td id="T_13005_row5_col5" class="data row5 col5" >0.2387</td>
-    </tr>
-    <tr>
-      <th id="T_13005_level0_row6" class="row_heading level0 row6" >[22743.76, 23890.64></th>
-      <td id="T_13005_row6_col0" class="data row6 col0" >13</td>
-      <td id="T_13005_row6_col1" class="data row6 col1" >77</td>
-      <td id="T_13005_row6_col2" class="data row6 col2" >5.50</td>
-      <td id="T_13005_row6_col3" class="data row6 col3" >6.31</td>
-      <td id="T_13005_row6_col4" class="data row6 col4" >5.38</td>
-      <td id="T_13005_row6_col5" class="data row6 col5" >0.1444</td>
-    </tr>
-    <tr>
-      <th id="T_13005_level0_row7" class="row_heading level0 row7" >>=23890.64</th>
-      <td id="T_13005_row7_col0" class="data row7 col0" >28</td>
-      <td id="T_13005_row7_col1" class="data row7 col1" >71</td>
-      <td id="T_13005_row7_col2" class="data row7 col2" >6.05</td>
-      <td id="T_13005_row7_col3" class="data row7 col3" >13.59</td>
-      <td id="T_13005_row7_col4" class="data row7 col4" >4.97</td>
-      <td id="T_13005_row7_col5" class="data row7 col5" >0.2828</td>
+      <th id="T_63f48_level0_row0" class="row_heading level0 row0" ><11684.56</th>
+      <td id="T_63f48_row0_col0" class="data row0 col0" >13</td>
+      <td id="T_63f48_row0_col1" class="data row0 col1" >423</td>
+      <td id="T_63f48_row0_col2" class="data row0 col2" >26.65</td>
+      <td id="T_63f48_row0_col3" class="data row0 col3" >6.31</td>
+      <td id="T_63f48_row0_col4" class="data row0 col4" >29.58</td>
+      <td id="T_63f48_row0_col5" class="data row0 col5" >0.0298</td>
+    </tr>
+    <tr>
+      <th id="T_63f48_level0_row1" class="row_heading level0 row1" >[11684.56, 13732.56></th>
+      <td id="T_63f48_row1_col0" class="data row1 col0" >24</td>
+      <td id="T_63f48_row1_col1" class="data row1 col1" >178</td>
+      <td id="T_63f48_row1_col2" class="data row1 col2" >12.35</td>
+      <td id="T_63f48_row1_col3" class="data row1 col3" >11.65</td>
+      <td id="T_63f48_row1_col4" class="data row1 col4" >12.45</td>
+      <td id="T_63f48_row1_col5" class="data row1 col5" >0.1188</td>
+    </tr>
+    <tr>
+      <th id="T_63f48_level0_row2" class="row_heading level0 row2" >[13732.56, 16845.52></th>
+      <td id="T_63f48_row2_col0" class="data row2 col0" >17</td>
+      <td id="T_63f48_row2_col1" class="data row2 col1" >250</td>
+      <td id="T_63f48_row2_col2" class="data row2 col2" >16.32</td>
+      <td id="T_63f48_row2_col3" class="data row2 col3" >8.25</td>
+      <td id="T_63f48_row2_col4" class="data row2 col4" >17.48</td>
+      <td id="T_63f48_row2_col5" class="data row2 col5" >0.0637</td>
+    </tr>
+    <tr>
+      <th id="T_63f48_level0_row3" class="row_heading level0 row3" >[16845.52, 19139.28></th>
+      <td id="T_63f48_row3_col0" class="data row3 col0" >51</td>
+      <td id="T_63f48_row3_col1" class="data row3 col1" >179</td>
+      <td id="T_63f48_row3_col2" class="data row3 col2" >14.06</td>
+      <td id="T_63f48_row3_col3" class="data row3 col3" >24.76</td>
+      <td id="T_63f48_row3_col4" class="data row3 col4" >12.52</td>
+      <td id="T_63f48_row3_col5" class="data row3 col5" >0.2217</td>
+    </tr>
+    <tr>
+      <th id="T_63f48_level0_row4" class="row_heading level0 row4" >[19139.28, 20286.16></th>
+      <td id="T_63f48_row4_col0" class="data row4 col0" >7</td>
+      <td id="T_63f48_row4_col1" class="data row4 col1" >83</td>
+      <td id="T_63f48_row4_col2" class="data row4 col2" >5.50</td>
+      <td id="T_63f48_row4_col3" class="data row4 col3" >3.40</td>
+      <td id="T_63f48_row4_col4" class="data row4 col4" >5.80</td>
+      <td id="T_63f48_row4_col5" class="data row4 col5" >0.0778</td>
+    </tr>
+    <tr>
+      <th id="T_63f48_level0_row5" class="row_heading level0 row5" >[20286.16, 22743.76></th>
+      <td id="T_63f48_row5_col0" class="data row5 col0" >53</td>
+      <td id="T_63f48_row5_col1" class="data row5 col1" >169</td>
+      <td id="T_63f48_row5_col2" class="data row5 col2" >13.57</td>
+      <td id="T_63f48_row5_col3" class="data row5 col3" >25.73</td>
+      <td id="T_63f48_row5_col4" class="data row5 col4" >11.82</td>
+      <td id="T_63f48_row5_col5" class="data row5 col5" >0.2387</td>
+    </tr>
+    <tr>
+      <th id="T_63f48_level0_row6" class="row_heading level0 row6" >[22743.76, 23890.64></th>
+      <td id="T_63f48_row6_col0" class="data row6 col0" >13</td>
+      <td id="T_63f48_row6_col1" class="data row6 col1" >77</td>
+      <td id="T_63f48_row6_col2" class="data row6 col2" >5.50</td>
+      <td id="T_63f48_row6_col3" class="data row6 col3" >6.31</td>
+      <td id="T_63f48_row6_col4" class="data row6 col4" >5.38</td>
+      <td id="T_63f48_row6_col5" class="data row6 col5" >0.1444</td>
+    </tr>
+    <tr>
+      <th id="T_63f48_level0_row7" class="row_heading level0 row7" >>=23890.64</th>
+      <td id="T_63f48_row7_col0" class="data row7 col0" >28</td>
+      <td id="T_63f48_row7_col1" class="data row7 col1" >71</td>
+      <td id="T_63f48_row7_col2" class="data row7 col2" >6.05</td>
+      <td id="T_63f48_row7_col3" class="data row7 col3" >13.59</td>
+      <td id="T_63f48_row7_col4" class="data row7 col4" >4.97</td>
+      <td id="T_63f48_row7_col5" class="data row7 col5" >0.2828</td>
     </tr>
   </tbody>
 </table></div>
@@ -903,17 +903,17 @@ <h3>Lift<a class="headerlink" href="#Lift" title="Link to this heading">¶</a></
 </div>
 <div class="output_area rendered_html docutils container">
 <style type="text/css">
-#T_69a9f_row0_col2, #T_69a9f_row1_col2, #T_69a9f_row2_col2, #T_69a9f_row3_col2, #T_69a9f_row4_col2, #T_69a9f_row5_col2, #T_69a9f_row6_col2, #T_69a9f_row7_col2 {
+#T_9de26_row0_col2, #T_9de26_row1_col2, #T_9de26_row2_col2, #T_9de26_row3_col2, #T_9de26_row4_col2, #T_9de26_row5_col2, #T_9de26_row6_col2, #T_9de26_row7_col2 {
   color: blue;
 }
 </style>
-<table id="T_69a9f">
+<table id="T_9de26">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_69a9f_level0_col0" class="col_heading level0 col0" >Positives</th>
-      <th id="T_69a9f_level0_col1" class="col_heading level0 col1" >Negatives</th>
-      <th id="T_69a9f_level0_col2" class="col_heading level0 col2" >Lift</th>
+      <th id="T_9de26_level0_col0" class="col_heading level0 col0" >Positives</th>
+      <th id="T_9de26_level0_col1" class="col_heading level0 col1" >Negatives</th>
+      <th id="T_9de26_level0_col2" class="col_heading level0 col2" >Lift</th>
     </tr>
     <tr>
       <th class="index_name level0" >Range/Symbol</th>
@@ -924,52 +924,52 @@ <h3>Lift<a class="headerlink" href="#Lift" title="Link to this heading">¶</a></
   </thead>
   <tbody>
     <tr>
-      <th id="T_69a9f_level0_row0" class="row_heading level0 row0" ><11684.56</th>
-      <td id="T_69a9f_row0_col0" class="data row0 col0" >13</td>
-      <td id="T_69a9f_row0_col1" class="data row0 col1" >423</td>
-      <td id="T_69a9f_row0_col2" class="data row0 col2" >0.2368</td>
+      <th id="T_9de26_level0_row0" class="row_heading level0 row0" ><11684.56</th>
+      <td id="T_9de26_row0_col0" class="data row0 col0" >13</td>
+      <td id="T_9de26_row0_col1" class="data row0 col1" >423</td>
+      <td id="T_9de26_row0_col2" class="data row0 col2" >0.2368</td>
     </tr>
     <tr>
-      <th id="T_69a9f_level0_row1" class="row_heading level0 row1" >[11684.56, 13732.56></th>
-      <td id="T_69a9f_row1_col0" class="data row1 col0" >24</td>
-      <td id="T_69a9f_row1_col1" class="data row1 col1" >178</td>
-      <td id="T_69a9f_row1_col2" class="data row1 col2" >0.9436</td>
+      <th id="T_9de26_level0_row1" class="row_heading level0 row1" >[11684.56, 13732.56></th>
+      <td id="T_9de26_row1_col0" class="data row1 col0" >24</td>
+      <td id="T_9de26_row1_col1" class="data row1 col1" >178</td>
+      <td id="T_9de26_row1_col2" class="data row1 col2" >0.9436</td>
     </tr>
     <tr>
-      <th id="T_69a9f_level0_row2" class="row_heading level0 row2" >[13732.56, 16845.52></th>
-      <td id="T_69a9f_row2_col0" class="data row2 col0" >17</td>
-      <td id="T_69a9f_row2_col1" class="data row2 col1" >250</td>
-      <td id="T_69a9f_row2_col2" class="data row2 col2" >0.5057</td>
+      <th id="T_9de26_level0_row2" class="row_heading level0 row2" >[13732.56, 16845.52></th>
+      <td id="T_9de26_row2_col0" class="data row2 col0" >17</td>
+      <td id="T_9de26_row2_col1" class="data row2 col1" >250</td>
+      <td id="T_9de26_row2_col2" class="data row2 col2" >0.5057</td>
     </tr>
     <tr>
-      <th id="T_69a9f_level0_row3" class="row_heading level0 row3" >[16845.52, 19139.28></th>
-      <td id="T_69a9f_row3_col0" class="data row3 col0" >51</td>
-      <td id="T_69a9f_row3_col1" class="data row3 col1" >179</td>
-      <td id="T_69a9f_row3_col2" class="data row3 col2" >1.7610</td>
+      <th id="T_9de26_level0_row3" class="row_heading level0 row3" >[16845.52, 19139.28></th>
+      <td id="T_9de26_row3_col0" class="data row3 col0" >51</td>
+      <td id="T_9de26_row3_col1" class="data row3 col1" >179</td>
+      <td id="T_9de26_row3_col2" class="data row3 col2" >1.7610</td>
     </tr>
     <tr>
-      <th id="T_69a9f_level0_row4" class="row_heading level0 row4" >[19139.28, 20286.16></th>
-      <td id="T_69a9f_row4_col0" class="data row4 col0" >7</td>
-      <td id="T_69a9f_row4_col1" class="data row4 col1" >83</td>
-      <td id="T_69a9f_row4_col2" class="data row4 col2" >0.6177</td>
+      <th id="T_9de26_level0_row4" class="row_heading level0 row4" >[19139.28, 20286.16></th>
+      <td id="T_9de26_row4_col0" class="data row4 col0" >7</td>
+      <td id="T_9de26_row4_col1" class="data row4 col1" >83</td>
+      <td id="T_9de26_row4_col2" class="data row4 col2" >0.6177</td>
     </tr>
     <tr>
-      <th id="T_69a9f_level0_row5" class="row_heading level0 row5" >[20286.16, 22743.76></th>
-      <td id="T_69a9f_row5_col0" class="data row5 col0" >53</td>
-      <td id="T_69a9f_row5_col1" class="data row5 col1" >169</td>
-      <td id="T_69a9f_row5_col2" class="data row5 col2" >1.8960</td>
+      <th id="T_9de26_level0_row5" class="row_heading level0 row5" >[20286.16, 22743.76></th>
+      <td id="T_9de26_row5_col0" class="data row5 col0" >53</td>
+      <td id="T_9de26_row5_col1" class="data row5 col1" >169</td>
+      <td id="T_9de26_row5_col2" class="data row5 col2" >1.8960</td>
     </tr>
     <tr>
-      <th id="T_69a9f_level0_row6" class="row_heading level0 row6" >[22743.76, 23890.64></th>
-      <td id="T_69a9f_row6_col0" class="data row6 col0" >13</td>
-      <td id="T_69a9f_row6_col1" class="data row6 col1" >77</td>
-      <td id="T_69a9f_row6_col2" class="data row6 col2" >1.1471</td>
+      <th id="T_9de26_level0_row6" class="row_heading level0 row6" >[22743.76, 23890.64></th>
+      <td id="T_9de26_row6_col0" class="data row6 col0" >13</td>
+      <td id="T_9de26_row6_col1" class="data row6 col1" >77</td>
+      <td id="T_9de26_row6_col2" class="data row6 col2" >1.1471</td>
     </tr>
     <tr>
-      <th id="T_69a9f_level0_row7" class="row_heading level0 row7" >>=23890.64</th>
-      <td id="T_69a9f_row7_col0" class="data row7 col0" >28</td>
-      <td id="T_69a9f_row7_col1" class="data row7 col1" >71</td>
-      <td id="T_69a9f_row7_col2" class="data row7 col2" >2.2462</td>
+      <th id="T_9de26_level0_row7" class="row_heading level0 row7" >>=23890.64</th>
+      <td id="T_9de26_row7_col0" class="data row7 col0" >28</td>
+      <td id="T_9de26_row7_col1" class="data row7 col1" >71</td>
+      <td id="T_9de26_row7_col2" class="data row7 col2" >2.2462</td>
     </tr>
   </tbody>
 </table></div>
@@ -1026,19 +1026,19 @@ <h3>Z-Ratio<a class="headerlink" href="#Z-Ratio" title="Link to this heading">¶
 </div>
 <div class="output_area rendered_html docutils container">
 <style type="text/css">
-#T_e50b4_row0_col4, #T_e50b4_row1_col4, #T_e50b4_row2_col4, #T_e50b4_row3_col4, #T_e50b4_row4_col4, #T_e50b4_row5_col4, #T_e50b4_row6_col4, #T_e50b4_row7_col4 {
+#T_2a0af_row0_col4, #T_2a0af_row1_col4, #T_2a0af_row2_col4, #T_2a0af_row3_col4, #T_2a0af_row4_col4, #T_2a0af_row5_col4, #T_2a0af_row6_col4, #T_2a0af_row7_col4 {
   color: blue;
 }
 </style>
-<table id="T_e50b4">
+<table id="T_2a0af">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_e50b4_level0_col0" class="col_heading level0 col0" >Positives</th>
-      <th id="T_e50b4_level0_col1" class="col_heading level0 col1" >Negatives</th>
-      <th id="T_e50b4_level0_col2" class="col_heading level0 col2" >Positives %</th>
-      <th id="T_e50b4_level0_col3" class="col_heading level0 col3" >Negatives %</th>
-      <th id="T_e50b4_level0_col4" class="col_heading level0 col4" >ZRatio</th>
+      <th id="T_2a0af_level0_col0" class="col_heading level0 col0" >Positives</th>
+      <th id="T_2a0af_level0_col1" class="col_heading level0 col1" >Negatives</th>
+      <th id="T_2a0af_level0_col2" class="col_heading level0 col2" >Positives %</th>
+      <th id="T_2a0af_level0_col3" class="col_heading level0 col3" >Negatives %</th>
+      <th id="T_2a0af_level0_col4" class="col_heading level0 col4" >ZRatio</th>
     </tr>
     <tr>
       <th class="index_name level0" >Range/Symbol</th>
@@ -1051,68 +1051,68 @@ <h3>Z-Ratio<a class="headerlink" href="#Z-Ratio" title="Link to this heading">¶
   </thead>
   <tbody>
     <tr>
-      <th id="T_e50b4_level0_row0" class="row_heading level0 row0" ><11684.56</th>
-      <td id="T_e50b4_row0_col0" class="data row0 col0" >13</td>
-      <td id="T_e50b4_row0_col1" class="data row0 col1" >423</td>
-      <td id="T_e50b4_row0_col2" class="data row0 col2" >6.31</td>
-      <td id="T_e50b4_row0_col3" class="data row0 col3" >29.58</td>
-      <td id="T_e50b4_row0_col4" class="data row0 col4" >-11.1869</td>
+      <th id="T_2a0af_level0_row0" class="row_heading level0 row0" ><11684.56</th>
+      <td id="T_2a0af_row0_col0" class="data row0 col0" >13</td>
+      <td id="T_2a0af_row0_col1" class="data row0 col1" >423</td>
+      <td id="T_2a0af_row0_col2" class="data row0 col2" >6.31</td>
+      <td id="T_2a0af_row0_col3" class="data row0 col3" >29.58</td>
+      <td id="T_2a0af_row0_col4" class="data row0 col4" >-11.1869</td>
     </tr>
     <tr>
-      <th id="T_e50b4_level0_row1" class="row_heading level0 row1" >[11684.56, 13732.56></th>
-      <td id="T_e50b4_row1_col0" class="data row1 col0" >24</td>
-      <td id="T_e50b4_row1_col1" class="data row1 col1" >178</td>
-      <td id="T_e50b4_row1_col2" class="data row1 col2" >11.65</td>
-      <td id="T_e50b4_row1_col3" class="data row1 col3" >12.45</td>
-      <td id="T_e50b4_row1_col4" class="data row1 col4" >-0.3321</td>
+      <th id="T_2a0af_level0_row1" class="row_heading level0 row1" >[11684.56, 13732.56></th>
+      <td id="T_2a0af_row1_col0" class="data row1 col0" >24</td>
+      <td id="T_2a0af_row1_col1" class="data row1 col1" >178</td>
+      <td id="T_2a0af_row1_col2" class="data row1 col2" >11.65</td>
+      <td id="T_2a0af_row1_col3" class="data row1 col3" >12.45</td>
+      <td id="T_2a0af_row1_col4" class="data row1 col4" >-0.3321</td>
     </tr>
     <tr>
-      <th id="T_e50b4_level0_row2" class="row_heading level0 row2" >[13732.56, 16845.52></th>
-      <td id="T_e50b4_row2_col0" class="data row2 col0" >17</td>
-      <td id="T_e50b4_row2_col1" class="data row2 col1" >250</td>
-      <td id="T_e50b4_row2_col2" class="data row2 col2" >8.25</td>
-      <td id="T_e50b4_row2_col3" class="data row2 col3" >17.48</td>
-      <td id="T_e50b4_row2_col4" class="data row2 col4" >-4.2647</td>
+      <th id="T_2a0af_level0_row2" class="row_heading level0 row2" >[13732.56, 16845.52></th>
+      <td id="T_2a0af_row2_col0" class="data row2 col0" >17</td>
+      <td id="T_2a0af_row2_col1" class="data row2 col1" >250</td>
+      <td id="T_2a0af_row2_col2" class="data row2 col2" >8.25</td>
+      <td id="T_2a0af_row2_col3" class="data row2 col3" >17.48</td>
+      <td id="T_2a0af_row2_col4" class="data row2 col4" >-4.2647</td>
     </tr>
     <tr>
-      <th id="T_e50b4_level0_row3" class="row_heading level0 row3" >[16845.52, 19139.28></th>
-      <td id="T_e50b4_row3_col0" class="data row3 col0" >51</td>
-      <td id="T_e50b4_row3_col1" class="data row3 col1" >179</td>
-      <td id="T_e50b4_row3_col2" class="data row3 col2" >24.76</td>
-      <td id="T_e50b4_row3_col3" class="data row3 col3" >12.52</td>
-      <td id="T_e50b4_row3_col4" class="data row3 col4" >3.9082</td>
+      <th id="T_2a0af_level0_row3" class="row_heading level0 row3" >[16845.52, 19139.28></th>
+      <td id="T_2a0af_row3_col0" class="data row3 col0" >51</td>
+      <td id="T_2a0af_row3_col1" class="data row3 col1" >179</td>
+      <td id="T_2a0af_row3_col2" class="data row3 col2" >24.76</td>
+      <td id="T_2a0af_row3_col3" class="data row3 col3" >12.52</td>
+      <td id="T_2a0af_row3_col4" class="data row3 col4" >3.9082</td>
     </tr>
     <tr>
-      <th id="T_e50b4_level0_row4" class="row_heading level0 row4" >[19139.28, 20286.16></th>
-      <td id="T_e50b4_row4_col0" class="data row4 col0" >7</td>
-      <td id="T_e50b4_row4_col1" class="data row4 col1" >83</td>
-      <td id="T_e50b4_row4_col2" class="data row4 col2" >3.40</td>
-      <td id="T_e50b4_row4_col3" class="data row4 col3" >5.80</td>
-      <td id="T_e50b4_row4_col4" class="data row4 col4" >-1.7118</td>
+      <th id="T_2a0af_level0_row4" class="row_heading level0 row4" >[19139.28, 20286.16></th>
+      <td id="T_2a0af_row4_col0" class="data row4 col0" >7</td>
+      <td id="T_2a0af_row4_col1" class="data row4 col1" >83</td>
+      <td id="T_2a0af_row4_col2" class="data row4 col2" >3.40</td>
+      <td id="T_2a0af_row4_col3" class="data row4 col3" >5.80</td>
+      <td id="T_2a0af_row4_col4" class="data row4 col4" >-1.7118</td>
     </tr>
     <tr>
-      <th id="T_e50b4_level0_row5" class="row_heading level0 row5" >[20286.16, 22743.76></th>
-      <td id="T_e50b4_row5_col0" class="data row5 col0" >53</td>
-      <td id="T_e50b4_row5_col1" class="data row5 col1" >169</td>
-      <td id="T_e50b4_row5_col2" class="data row5 col2" >25.73</td>
-      <td id="T_e50b4_row5_col3" class="data row5 col3" >11.82</td>
-      <td id="T_e50b4_row5_col4" class="data row5 col4" >4.3976</td>
+      <th id="T_2a0af_level0_row5" class="row_heading level0 row5" >[20286.16, 22743.76></th>
+      <td id="T_2a0af_row5_col0" class="data row5 col0" >53</td>
+      <td id="T_2a0af_row5_col1" class="data row5 col1" >169</td>
+      <td id="T_2a0af_row5_col2" class="data row5 col2" >25.73</td>
+      <td id="T_2a0af_row5_col3" class="data row5 col3" >11.82</td>
+      <td id="T_2a0af_row5_col4" class="data row5 col4" >4.3976</td>
     </tr>
     <tr>
-      <th id="T_e50b4_level0_row6" class="row_heading level0 row6" >[22743.76, 23890.64></th>
-      <td id="T_e50b4_row6_col0" class="data row6 col0" >13</td>
-      <td id="T_e50b4_row6_col1" class="data row6 col1" >77</td>
-      <td id="T_e50b4_row6_col2" class="data row6 col2" >6.31</td>
-      <td id="T_e50b4_row6_col3" class="data row6 col3" >5.38</td>
-      <td id="T_e50b4_row6_col4" class="data row6 col4" >0.5156</td>
+      <th id="T_2a0af_level0_row6" class="row_heading level0 row6" >[22743.76, 23890.64></th>
+      <td id="T_2a0af_row6_col0" class="data row6 col0" >13</td>
+      <td id="T_2a0af_row6_col1" class="data row6 col1" >77</td>
+      <td id="T_2a0af_row6_col2" class="data row6 col2" >6.31</td>
+      <td id="T_2a0af_row6_col3" class="data row6 col3" >5.38</td>
+      <td id="T_2a0af_row6_col4" class="data row6 col4" >0.5156</td>
     </tr>
     <tr>
-      <th id="T_e50b4_level0_row7" class="row_heading level0 row7" >>=23890.64</th>
-      <td id="T_e50b4_row7_col0" class="data row7 col0" >28</td>
-      <td id="T_e50b4_row7_col1" class="data row7 col1" >71</td>
-      <td id="T_e50b4_row7_col2" class="data row7 col2" >13.59</td>
-      <td id="T_e50b4_row7_col3" class="data row7 col3" >4.97</td>
-      <td id="T_e50b4_row7_col4" class="data row7 col4" >3.5129</td>
+      <th id="T_2a0af_level0_row7" class="row_heading level0 row7" >>=23890.64</th>
+      <td id="T_2a0af_row7_col0" class="data row7 col0" >28</td>
+      <td id="T_2a0af_row7_col1" class="data row7 col1" >71</td>
+      <td id="T_2a0af_row7_col2" class="data row7 col2" >13.59</td>
+      <td id="T_2a0af_row7_col3" class="data row7 col3" >4.97</td>
+      <td id="T_2a0af_row7_col4" class="data row7 col4" >3.5129</td>
     </tr>
   </tbody>
 </table></div>
@@ -1163,9 +1163,9 @@ <h2>Predictor AUC<a class="headerlink" href="#Predictor-AUC" title="Link to this
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area rendered_html docutils container">
-<div>                            <div id="2f661812-04d3-4ae7-8d40-110161cfb4ec" class="plotly-graph-div" style="height:700px; width:700px;"></div>            <script type="text/javascript">                require(["plotly"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById("2f661812-04d3-4ae7-8d40-110161cfb4ec")) {                    Plotly.newPlot(                        "2f661812-04d3-4ae7-8d40-110161cfb4ec",                        [{"hovertemplate":"Specificity=%{x}\u003cbr\u003eSensitivity=%{y}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"","line":{"color":"#1F77B4","dash":"solid"},"marker":{"symbol":"circle"},"mode":"lines","name":"","orientation":"v","showlegend":false,"x":[1.0,0.9368932247161865,0.8543689250946045,0.8203883171081543,0.7038835287094116,0.6407766938209534,0.39320385456085205,0.13592231273651123,0.0],"xaxis":"x","y":[0.0,0.2958042025566101,0.47062936425209045,0.5286713242530823,0.6531468629837036,0.7069929838180542,0.8321678042411804,0.9503496289253235,1.0],"yaxis":"y","type":"scatter"}],                        {"template":{"data":{"scatter":[{"type":"scatter"}]}},"xaxis":{"anchor":"y","domain":[0.0,1.0],"title":{"text":"Specificity"},"range":[1,0]},"yaxis":{"anchor":"x","domain":[0.0,1.0],"title":{"text":"Sensitivity"}},"legend":{"tracegroupgap":0},"title":{"text":"AUC = 0.722"},"height":700,"width":700,"shapes":[{"line":{"dash":"dash"},"type":"line","x0":1,"x1":0,"y0":0,"y1":1}]},                        {"responsive": true}                    ).then(function(){
+<div>                            <div id="70112958-3d75-4db1-b602-02d61e635918" class="plotly-graph-div" style="height:700px; width:700px;"></div>            <script type="text/javascript">                require(["plotly"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById("70112958-3d75-4db1-b602-02d61e635918")) {                    Plotly.newPlot(                        "70112958-3d75-4db1-b602-02d61e635918",                        [{"hovertemplate":"Specificity=%{x}\u003cbr\u003eSensitivity=%{y}\u003cextra\u003e\u003c\u002fextra\u003e","legendgroup":"","line":{"color":"#1F77B4","dash":"solid"},"marker":{"symbol":"circle"},"mode":"lines","name":"","orientation":"v","showlegend":false,"x":[1.0,0.9368932247161865,0.8543689250946045,0.8203883171081543,0.7038835287094116,0.6407766938209534,0.39320385456085205,0.13592231273651123,0.0],"xaxis":"x","y":[0.0,0.2958042025566101,0.47062936425209045,0.5286713242530823,0.6531468629837036,0.7069929838180542,0.8321678042411804,0.9503496289253235,1.0],"yaxis":"y","type":"scatter"}],                        {"template":{"data":{"scatter":[{"type":"scatter"}]}},"xaxis":{"anchor":"y","domain":[0.0,1.0],"title":{"text":"Specificity"},"range":[1,0]},"yaxis":{"anchor":"x","domain":[0.0,1.0],"title":{"text":"Sensitivity"}},"legend":{"tracegroupgap":0},"title":{"text":"AUC = 0.722"},"height":700,"width":700,"shapes":[{"line":{"dash":"dash"},"type":"line","x0":1,"x1":0,"y0":0,"y1":1}]},                        {"responsive": true}                    ).then(function(){
 
-var gd = document.getElementById('2f661812-04d3-4ae7-8d40-110161cfb4ec');
+var gd = document.getElementById('70112958-3d75-4db1-b602-02d61e635918');
 var x = new MutationObserver(function (mutations, observer) {{
         var display = window.getComputedStyle(gd).display;
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@@ -1240,20 +1240,20 @@ <h2>Predictor Contribution<a class="headerlink" href="#Predictor-Contribution" t
 </div>
 <div class="output_area rendered_html docutils container">
 <style type="text/css">
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+#T_f77c7_row0_col4, #T_f77c7_row0_col5, #T_f77c7_row1_col4, #T_f77c7_row1_col5, #T_f77c7_row2_col4, #T_f77c7_row2_col5, #T_f77c7_row3_col4, #T_f77c7_row3_col5, #T_f77c7_row4_col4, #T_f77c7_row4_col5, #T_f77c7_row5_col4, #T_f77c7_row5_col5, #T_f77c7_row6_col4, #T_f77c7_row6_col5, #T_f77c7_row7_col4, #T_f77c7_row7_col5 {
   color: blue;
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 </style>
-<table id="T_2980d">
+<table id="T_f77c7">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_2980d_level0_col0" class="col_heading level0 col0" >Positives</th>
-      <th id="T_2980d_level0_col1" class="col_heading level0 col1" >Negatives</th>
-      <th id="T_2980d_level0_col2" class="col_heading level0 col2" >Positives %</th>
-      <th id="T_2980d_level0_col3" class="col_heading level0 col3" >Negatives %</th>
-      <th id="T_2980d_level0_col4" class="col_heading level0 col4" >LogOdds</th>
-      <th id="T_2980d_level0_col5" class="col_heading level0 col5" >ModifiedLogOdds</th>
+      <th id="T_f77c7_level0_col0" class="col_heading level0 col0" >Positives</th>
+      <th id="T_f77c7_level0_col1" class="col_heading level0 col1" >Negatives</th>
+      <th id="T_f77c7_level0_col2" class="col_heading level0 col2" >Positives %</th>
+      <th id="T_f77c7_level0_col3" class="col_heading level0 col3" >Negatives %</th>
+      <th id="T_f77c7_level0_col4" class="col_heading level0 col4" >LogOdds</th>
+      <th id="T_f77c7_level0_col5" class="col_heading level0 col5" >ModifiedLogOdds</th>
     </tr>
     <tr>
       <th class="index_name level0" >Range/Symbol</th>
@@ -1267,76 +1267,76 @@ <h2>Predictor Contribution<a class="headerlink" href="#Predictor-Contribution" t
   </thead>
   <tbody>
     <tr>
-      <th id="T_2980d_level0_row0" class="row_heading level0 row0" ><11684.56</th>
-      <td id="T_2980d_row0_col0" class="data row0 col0" >13</td>
-      <td id="T_2980d_row0_col1" class="data row0 col1" >423</td>
-      <td id="T_2980d_row0_col2" class="data row0 col2" >6.31</td>
-      <td id="T_2980d_row0_col3" class="data row0 col3" >29.580000</td>
-      <td id="T_2980d_row0_col4" class="data row0 col4" >-1.544963</td>
-      <td id="T_2980d_row0_col5" class="data row0 col5" >-1.5397</td>
-    </tr>
-    <tr>
-      <th id="T_2980d_level0_row1" class="row_heading level0 row1" >[11684.56, 13732.56></th>
-      <td id="T_2980d_row1_col0" class="data row1 col0" >24</td>
-      <td id="T_2980d_row1_col1" class="data row1 col1" >178</td>
-      <td id="T_2980d_row1_col2" class="data row1 col2" >11.65</td>
-      <td id="T_2980d_row1_col3" class="data row1 col3" >12.450000</td>
-      <td id="T_2980d_row1_col4" class="data row1 col4" >-0.066414</td>
-      <td id="T_2980d_row1_col5" class="data row1 col5" >-0.0658</td>
-    </tr>
-    <tr>
-      <th id="T_2980d_level0_row2" class="row_heading level0 row2" >[13732.56, 16845.52></th>
-      <td id="T_2980d_row2_col0" class="data row2 col0" >17</td>
-      <td id="T_2980d_row2_col1" class="data row2 col1" >250</td>
-      <td id="T_2980d_row2_col2" class="data row2 col2" >8.25</td>
-      <td id="T_2980d_row2_col3" class="data row2 col3" >17.480000</td>
-      <td id="T_2980d_row2_col4" class="data row2 col4" >-0.750844</td>
-      <td id="T_2980d_row2_col5" class="data row2 col5" >-0.7480</td>
-    </tr>
-    <tr>
-      <th id="T_2980d_level0_row3" class="row_heading level0 row3" >[16845.52, 19139.28></th>
-      <td id="T_2980d_row3_col0" class="data row3 col0" >51</td>
-      <td id="T_2980d_row3_col1" class="data row3 col1" >179</td>
-      <td id="T_2980d_row3_col2" class="data row3 col2" >24.76</td>
-      <td id="T_2980d_row3_col3" class="data row3 col3" >12.520000</td>
-      <td id="T_2980d_row3_col4" class="data row3 col4" >0.681902</td>
-      <td id="T_2980d_row3_col5" class="data row3 col5" >0.6796</td>
-    </tr>
-    <tr>
-      <th id="T_2980d_level0_row4" class="row_heading level0 row4" >[19139.28, 20286.16></th>
-      <td id="T_2980d_row4_col0" class="data row4 col0" >7</td>
-      <td id="T_2980d_row4_col1" class="data row4 col1" >83</td>
-      <td id="T_2980d_row4_col2" class="data row4 col2" >3.40</td>
-      <td id="T_2980d_row4_col3" class="data row4 col3" >5.800000</td>
-      <td id="T_2980d_row4_col4" class="data row4 col4" >-0.534083</td>
-      <td id="T_2980d_row4_col5" class="data row4 col5" >-0.5233</td>
-    </tr>
-    <tr>
-      <th id="T_2980d_level0_row5" class="row_heading level0 row5" >[20286.16, 22743.76></th>
-      <td id="T_2980d_row5_col0" class="data row5 col0" >53</td>
-      <td id="T_2980d_row5_col1" class="data row5 col1" >169</td>
-      <td id="T_2980d_row5_col2" class="data row5 col2" >25.73</td>
-      <td id="T_2980d_row5_col3" class="data row5 col3" >11.820000</td>
-      <td id="T_2980d_row5_col4" class="data row5 col4" >0.777865</td>
-      <td id="T_2980d_row5_col5" class="data row5 col5" >0.7754</td>
-    </tr>
-    <tr>
-      <th id="T_2980d_level0_row6" class="row_heading level0 row6" >[22743.76, 23890.64></th>
-      <td id="T_2980d_row6_col0" class="data row6 col0" >13</td>
-      <td id="T_2980d_row6_col1" class="data row6 col1" >77</td>
-      <td id="T_2980d_row6_col2" class="data row6 col2" >6.31</td>
-      <td id="T_2980d_row6_col3" class="data row6 col3" >5.380000</td>
-      <td id="T_2980d_row6_col4" class="data row6 col4" >0.159447</td>
-      <td id="T_2980d_row6_col5" class="data row6 col5" >0.1625</td>
-    </tr>
-    <tr>
-      <th id="T_2980d_level0_row7" class="row_heading level0 row7" >>=23890.64</th>
-      <td id="T_2980d_row7_col0" class="data row7 col0" >28</td>
-      <td id="T_2980d_row7_col1" class="data row7 col1" >71</td>
-      <td id="T_2980d_row7_col2" class="data row7 col2" >13.59</td>
-      <td id="T_2980d_row7_col3" class="data row7 col3" >4.970000</td>
-      <td id="T_2980d_row7_col4" class="data row7 col4" >1.005915</td>
-      <td id="T_2980d_row7_col5" class="data row7 col5" >1.0056</td>
+      <th id="T_f77c7_level0_row0" class="row_heading level0 row0" ><11684.56</th>
+      <td id="T_f77c7_row0_col0" class="data row0 col0" >13</td>
+      <td id="T_f77c7_row0_col1" class="data row0 col1" >423</td>
+      <td id="T_f77c7_row0_col2" class="data row0 col2" >6.31</td>
+      <td id="T_f77c7_row0_col3" class="data row0 col3" >29.580000</td>
+      <td id="T_f77c7_row0_col4" class="data row0 col4" >-1.544963</td>
+      <td id="T_f77c7_row0_col5" class="data row0 col5" >-1.5397</td>
+    </tr>
+    <tr>
+      <th id="T_f77c7_level0_row1" class="row_heading level0 row1" >[11684.56, 13732.56></th>
+      <td id="T_f77c7_row1_col0" class="data row1 col0" >24</td>
+      <td id="T_f77c7_row1_col1" class="data row1 col1" >178</td>
+      <td id="T_f77c7_row1_col2" class="data row1 col2" >11.65</td>
+      <td id="T_f77c7_row1_col3" class="data row1 col3" >12.450000</td>
+      <td id="T_f77c7_row1_col4" class="data row1 col4" >-0.066414</td>
+      <td id="T_f77c7_row1_col5" class="data row1 col5" >-0.0658</td>
+    </tr>
+    <tr>
+      <th id="T_f77c7_level0_row2" class="row_heading level0 row2" >[13732.56, 16845.52></th>
+      <td id="T_f77c7_row2_col0" class="data row2 col0" >17</td>
+      <td id="T_f77c7_row2_col1" class="data row2 col1" >250</td>
+      <td id="T_f77c7_row2_col2" class="data row2 col2" >8.25</td>
+      <td id="T_f77c7_row2_col3" class="data row2 col3" >17.480000</td>
+      <td id="T_f77c7_row2_col4" class="data row2 col4" >-0.750844</td>
+      <td id="T_f77c7_row2_col5" class="data row2 col5" >-0.7480</td>
+    </tr>
+    <tr>
+      <th id="T_f77c7_level0_row3" class="row_heading level0 row3" >[16845.52, 19139.28></th>
+      <td id="T_f77c7_row3_col0" class="data row3 col0" >51</td>
+      <td id="T_f77c7_row3_col1" class="data row3 col1" >179</td>
+      <td id="T_f77c7_row3_col2" class="data row3 col2" >24.76</td>
+      <td id="T_f77c7_row3_col3" class="data row3 col3" >12.520000</td>
+      <td id="T_f77c7_row3_col4" class="data row3 col4" >0.681902</td>
+      <td id="T_f77c7_row3_col5" class="data row3 col5" >0.6796</td>
+    </tr>
+    <tr>
+      <th id="T_f77c7_level0_row4" class="row_heading level0 row4" >[19139.28, 20286.16></th>
+      <td id="T_f77c7_row4_col0" class="data row4 col0" >7</td>
+      <td id="T_f77c7_row4_col1" class="data row4 col1" >83</td>
+      <td id="T_f77c7_row4_col2" class="data row4 col2" >3.40</td>
+      <td id="T_f77c7_row4_col3" class="data row4 col3" >5.800000</td>
+      <td id="T_f77c7_row4_col4" class="data row4 col4" >-0.534083</td>
+      <td id="T_f77c7_row4_col5" class="data row4 col5" >-0.5233</td>
+    </tr>
+    <tr>
+      <th id="T_f77c7_level0_row5" class="row_heading level0 row5" >[20286.16, 22743.76></th>
+      <td id="T_f77c7_row5_col0" class="data row5 col0" >53</td>
+      <td id="T_f77c7_row5_col1" class="data row5 col1" >169</td>
+      <td id="T_f77c7_row5_col2" class="data row5 col2" >25.73</td>
+      <td id="T_f77c7_row5_col3" class="data row5 col3" >11.820000</td>
+      <td id="T_f77c7_row5_col4" class="data row5 col4" >0.777865</td>
+      <td id="T_f77c7_row5_col5" class="data row5 col5" >0.7754</td>
+    </tr>
+    <tr>
+      <th id="T_f77c7_level0_row6" class="row_heading level0 row6" >[22743.76, 23890.64></th>
+      <td id="T_f77c7_row6_col0" class="data row6 col0" >13</td>
+      <td id="T_f77c7_row6_col1" class="data row6 col1" >77</td>
+      <td id="T_f77c7_row6_col2" class="data row6 col2" >6.31</td>
+      <td id="T_f77c7_row6_col3" class="data row6 col3" >5.380000</td>
+      <td id="T_f77c7_row6_col4" class="data row6 col4" >0.159447</td>
+      <td id="T_f77c7_row6_col5" class="data row6 col5" >0.1625</td>
+    </tr>
+    <tr>
+      <th id="T_f77c7_level0_row7" class="row_heading level0 row7" >>=23890.64</th>
+      <td id="T_f77c7_row7_col0" class="data row7 col0" >28</td>
+      <td id="T_f77c7_row7_col1" class="data row7 col1" >71</td>
+      <td id="T_f77c7_row7_col2" class="data row7 col2" >13.59</td>
+      <td id="T_f77c7_row7_col3" class="data row7 col3" >4.970000</td>
+      <td id="T_f77c7_row7_col4" class="data row7 col4" >1.005915</td>
+      <td id="T_f77c7_row7_col5" class="data row7 col5" >1.0056</td>
     </tr>
   </tbody>
 </table></div>
@@ -1358,15 +1358,15 @@ <h3>Log odds contribution for all the predictors<a class="headerlink" href="#Log
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-/tmp/ipykernel_2994/3077675025.py:49: DeprecationWarning:
+/tmp/ipykernel_2930/3077675025.py:49: DeprecationWarning:
 
 `where` is deprecated. Use `filter` instead.
 
-/tmp/ipykernel_2994/3077675025.py:51: DeprecationWarning:
+/tmp/ipykernel_2930/3077675025.py:51: DeprecationWarning:
 
 `where` is deprecated. Use `filter` instead.
 
-/tmp/ipykernel_2994/3077675025.py:52: DeprecationWarning:
+/tmp/ipykernel_2930/3077675025.py:52: DeprecationWarning:
 
 `where` is deprecated. Use `filter` instead.
 
@@ -1378,19 +1378,19 @@ <h3>Log odds contribution for all the predictors<a class="headerlink" href="#Log
 </div>
 <div class="output_area rendered_html docutils container">
 <style type="text/css">
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+#T_85bcd_row0_col4, #T_85bcd_row1_col4, #T_85bcd_row2_col4, #T_85bcd_row3_col4, #T_85bcd_row4_col4, #T_85bcd_row5_col4, #T_85bcd_row6_col4, #T_85bcd_row7_col4, #T_85bcd_row8_col4, #T_85bcd_row9_col4, #T_85bcd_row10_col4, #T_85bcd_row11_col4, #T_85bcd_row12_col4, #T_85bcd_row13_col4, #T_85bcd_row14_col4, #T_85bcd_row15_col4, #T_85bcd_row16_col4, #T_85bcd_row17_col4, #T_85bcd_row18_col4, #T_85bcd_row19_col4, #T_85bcd_row20_col4, #T_85bcd_row21_col4, #T_85bcd_row22_col4, #T_85bcd_row23_col4, #T_85bcd_row24_col4, #T_85bcd_row25_col4, #T_85bcd_row26_col4, #T_85bcd_row27_col4, #T_85bcd_row28_col4, #T_85bcd_row29_col4, #T_85bcd_row30_col4, #T_85bcd_row31_col4, #T_85bcd_row32_col4, #T_85bcd_row33_col4, #T_85bcd_row34_col4, #T_85bcd_row35_col4, #T_85bcd_row36_col4 {
   color: blue;
 }
 </style>
-<table id="T_877ee">
+<table id="T_85bcd">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_877ee_level0_col0" class="col_heading level0 col0" >Value</th>
-      <th id="T_877ee_level0_col1" class="col_heading level0 col1" >Bin</th>
-      <th id="T_877ee_level0_col2" class="col_heading level0 col2" >Positives</th>
-      <th id="T_877ee_level0_col3" class="col_heading level0 col3" >Negatives</th>
-      <th id="T_877ee_level0_col4" class="col_heading level0 col4" >LogOdds</th>
+      <th id="T_85bcd_level0_col0" class="col_heading level0 col0" >Value</th>
+      <th id="T_85bcd_level0_col1" class="col_heading level0 col1" >Bin</th>
+      <th id="T_85bcd_level0_col2" class="col_heading level0 col2" >Positives</th>
+      <th id="T_85bcd_level0_col3" class="col_heading level0 col3" >Negatives</th>
+      <th id="T_85bcd_level0_col4" class="col_heading level0 col4" >LogOdds</th>
     </tr>
     <tr>
       <th class="index_name level0" >PredictorName</th>
@@ -1403,300 +1403,300 @@ <h3>Log odds contribution for all the predictors<a class="headerlink" href="#Log
   </thead>
   <tbody>
     <tr>
-      <th id="T_877ee_level0_row0" class="row_heading level0 row0" >Customer.Age</th>
-      <td id="T_877ee_row0_col0" class="data row0 col0" >34.56</td>
-      <td id="T_877ee_row0_col1" class="data row0 col1" >4.000000</td>
-      <td id="T_877ee_row0_col2" class="data row0 col2" >9.000000</td>
-      <td id="T_877ee_row0_col3" class="data row0 col3" >198.000000</td>
-      <td id="T_877ee_row0_col4" class="data row0 col4" >-1.145923</td>
+      <th id="T_85bcd_level0_row0" class="row_heading level0 row0" >Customer.Age</th>
+      <td id="T_85bcd_row0_col0" class="data row0 col0" >34.56</td>
+      <td id="T_85bcd_row0_col1" class="data row0 col1" >4.000000</td>
+      <td id="T_85bcd_row0_col2" class="data row0 col2" >9.000000</td>
+      <td id="T_85bcd_row0_col3" class="data row0 col3" >198.000000</td>
+      <td id="T_85bcd_row0_col4" class="data row0 col4" >-1.145923</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row1" class="row_heading level0 row1" >Customer.AnnualIncome</th>
-      <td id="T_877ee_row1_col0" class="data row1 col0" >-24043.049</td>
-      <td id="T_877ee_row1_col1" class="data row1 col1" >1.000000</td>
-      <td id="T_877ee_row1_col2" class="data row1 col2" >74.000000</td>
-      <td id="T_877ee_row1_col3" class="data row1 col3" >1166.000000</td>
-      <td id="T_877ee_row1_col4" class="data row1 col4" >-0.819651</td>
+      <th id="T_85bcd_level0_row1" class="row_heading level0 row1" >Customer.AnnualIncome</th>
+      <td id="T_85bcd_row1_col0" class="data row1 col0" >-24043.049</td>
+      <td id="T_85bcd_row1_col1" class="data row1 col1" >1.000000</td>
+      <td id="T_85bcd_row1_col2" class="data row1 col2" >74.000000</td>
+      <td id="T_85bcd_row1_col3" class="data row1 col3" >1166.000000</td>
+      <td id="T_85bcd_row1_col4" class="data row1 col4" >-0.819651</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row2" class="row_heading level0 row2" >Customer.BusinessSegment</th>
-      <td id="T_877ee_row2_col0" class="data row2 col0" >middleSegmentPlus</td>
-      <td id="T_877ee_row2_col1" class="data row2 col1" >1.000000</td>
-      <td id="T_877ee_row2_col2" class="data row2 col2" >96.000000</td>
-      <td id="T_877ee_row2_col3" class="data row2 col3" >970.000000</td>
-      <td id="T_877ee_row2_col4" class="data row2 col4" >-0.376415</td>
+      <th id="T_85bcd_level0_row2" class="row_heading level0 row2" >Customer.BusinessSegment</th>
+      <td id="T_85bcd_row2_col0" class="data row2 col0" >middleSegmentPlus</td>
+      <td id="T_85bcd_row2_col1" class="data row2 col1" >1.000000</td>
+      <td id="T_85bcd_row2_col2" class="data row2 col2" >96.000000</td>
+      <td id="T_85bcd_row2_col3" class="data row2 col3" >970.000000</td>
+      <td id="T_85bcd_row2_col4" class="data row2 col4" >-0.376415</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row3" class="row_heading level0 row3" >Customer.CLV</th>
-      <td id="T_877ee_row3_col0" class="data row3 col0" >NON-MISSING</td>
-      <td id="T_877ee_row3_col1" class="data row3 col1" >1.000000</td>
-      <td id="T_877ee_row3_col2" class="data row3 col2" >111.000000</td>
-      <td id="T_877ee_row3_col3" class="data row3 col3" >570.000000</td>
-      <td id="T_877ee_row3_col4" class="data row3 col4" >0.300922</td>
+      <th id="T_85bcd_level0_row3" class="row_heading level0 row3" >Customer.CLV</th>
+      <td id="T_85bcd_row3_col0" class="data row3 col0" >NON-MISSING</td>
+      <td id="T_85bcd_row3_col1" class="data row3 col1" >1.000000</td>
+      <td id="T_85bcd_row3_col2" class="data row3 col2" >111.000000</td>
+      <td id="T_85bcd_row3_col3" class="data row3 col3" >570.000000</td>
+      <td id="T_85bcd_row3_col4" class="data row3 col4" >0.300922</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row4" class="row_heading level0 row4" >Customer.CLV_VALUE</th>
-      <td id="T_877ee_row4_col0" class="data row4 col0" >1345.52</td>
-      <td id="T_877ee_row4_col1" class="data row4 col1" >4.000000</td>
-      <td id="T_877ee_row4_col2" class="data row4 col2" >31.000000</td>
-      <td id="T_877ee_row4_col3" class="data row4 col3" >297.000000</td>
-      <td id="T_877ee_row4_col4" class="data row4 col4" >-0.322731</td>
+      <th id="T_85bcd_level0_row4" class="row_heading level0 row4" >Customer.CLV_VALUE</th>
+      <td id="T_85bcd_row4_col0" class="data row4 col0" >1345.52</td>
+      <td id="T_85bcd_row4_col1" class="data row4 col1" >4.000000</td>
+      <td id="T_85bcd_row4_col2" class="data row4 col2" >31.000000</td>
+      <td id="T_85bcd_row4_col3" class="data row4 col3" >297.000000</td>
+      <td id="T_85bcd_row4_col4" class="data row4 col4" >-0.322731</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row5" class="row_heading level0 row5" >Customer.CreditScore</th>
-      <td id="T_877ee_row5_col0" class="data row5 col0" >518.92</td>
-      <td id="T_877ee_row5_col1" class="data row5 col1" >3.000000</td>
-      <td id="T_877ee_row5_col2" class="data row5 col2" >33.000000</td>
-      <td id="T_877ee_row5_col3" class="data row5 col3" >205.000000</td>
-      <td id="T_877ee_row5_col4" class="data row5 col4" >0.110531</td>
+      <th id="T_85bcd_level0_row5" class="row_heading level0 row5" >Customer.CreditScore</th>
+      <td id="T_85bcd_row5_col0" class="data row5 col0" >518.92</td>
+      <td id="T_85bcd_row5_col1" class="data row5 col1" >3.000000</td>
+      <td id="T_85bcd_row5_col2" class="data row5 col2" >33.000000</td>
+      <td id="T_85bcd_row5_col3" class="data row5 col3" >205.000000</td>
+      <td id="T_85bcd_row5_col4" class="data row5 col4" >0.110531</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row6" class="row_heading level0 row6" >Customer.Date_of_Birth</th>
-      <td id="T_877ee_row6_col0" class="data row6 col0" >18773.504</td>
-      <td id="T_877ee_row6_col1" class="data row6 col1" >5.000000</td>
-      <td id="T_877ee_row6_col2" class="data row6 col2" >28.000000</td>
-      <td id="T_877ee_row6_col3" class="data row6 col3" >152.000000</td>
-      <td id="T_877ee_row6_col4" class="data row6 col4" >0.244642</td>
+      <th id="T_85bcd_level0_row6" class="row_heading level0 row6" >Customer.Date_of_Birth</th>
+      <td id="T_85bcd_row6_col0" class="data row6 col0" >18773.504</td>
+      <td id="T_85bcd_row6_col1" class="data row6 col1" >5.000000</td>
+      <td id="T_85bcd_row6_col2" class="data row6 col2" >28.000000</td>
+      <td id="T_85bcd_row6_col3" class="data row6 col3" >152.000000</td>
+      <td id="T_85bcd_row6_col4" class="data row6 col4" >0.244642</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row7" class="row_heading level0 row7" >Customer.Gender</th>
-      <td id="T_877ee_row7_col0" class="data row7 col0" >U</td>
-      <td id="T_877ee_row7_col1" class="data row7 col1" >1.000000</td>
-      <td id="T_877ee_row7_col2" class="data row7 col2" >52.000000</td>
-      <td id="T_877ee_row7_col3" class="data row7 col3" >481.000000</td>
-      <td id="T_877ee_row7_col4" class="data row7 col4" >-0.285516</td>
+      <th id="T_85bcd_level0_row7" class="row_heading level0 row7" >Customer.Gender</th>
+      <td id="T_85bcd_row7_col0" class="data row7 col0" >U</td>
+      <td id="T_85bcd_row7_col1" class="data row7 col1" >1.000000</td>
+      <td id="T_85bcd_row7_col2" class="data row7 col2" >52.000000</td>
+      <td id="T_85bcd_row7_col3" class="data row7 col3" >481.000000</td>
+      <td id="T_85bcd_row7_col4" class="data row7 col4" >-0.285516</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row8" class="row_heading level0 row8" >Customer.MaritalStatus</th>
-      <td id="T_877ee_row8_col0" class="data row8 col0" >No Resp+</td>
-      <td id="T_877ee_row8_col1" class="data row8 col1" >1.000000</td>
-      <td id="T_877ee_row8_col2" class="data row8 col2" >67.000000</td>
-      <td id="T_877ee_row8_col3" class="data row8 col3" >745.000000</td>
-      <td id="T_877ee_row8_col4" class="data row8 col4" >-0.470766</td>
+      <th id="T_85bcd_level0_row8" class="row_heading level0 row8" >Customer.MaritalStatus</th>
+      <td id="T_85bcd_row8_col0" class="data row8 col0" >No Resp+</td>
+      <td id="T_85bcd_row8_col1" class="data row8 col1" >1.000000</td>
+      <td id="T_85bcd_row8_col2" class="data row8 col2" >67.000000</td>
+      <td id="T_85bcd_row8_col3" class="data row8 col3" >745.000000</td>
+      <td id="T_85bcd_row8_col4" class="data row8 col4" >-0.470766</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row9" class="row_heading level0 row9" >Customer.NetWealth</th>
-      <td id="T_877ee_row9_col0" class="data row9 col0" >17992.398</td>
-      <td id="T_877ee_row9_col1" class="data row9 col1" >4.000000</td>
-      <td id="T_877ee_row9_col2" class="data row9 col2" >51.000000</td>
-      <td id="T_877ee_row9_col3" class="data row9 col3" >179.000000</td>
-      <td id="T_877ee_row9_col4" class="data row9 col4" >0.679600</td>
+      <th id="T_85bcd_level0_row9" class="row_heading level0 row9" >Customer.NetWealth</th>
+      <td id="T_85bcd_row9_col0" class="data row9 col0" >17992.398</td>
+      <td id="T_85bcd_row9_col1" class="data row9 col1" >4.000000</td>
+      <td id="T_85bcd_row9_col2" class="data row9 col2" >51.000000</td>
+      <td id="T_85bcd_row9_col3" class="data row9 col3" >179.000000</td>
+      <td id="T_85bcd_row9_col4" class="data row9 col4" >0.679600</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row10" class="row_heading level0 row10" >Customer.NoOfDependents</th>
-      <td id="T_877ee_row10_col0" class="data row10 col0" >0.0</td>
-      <td id="T_877ee_row10_col1" class="data row10 col1" >1.000000</td>
-      <td id="T_877ee_row10_col2" class="data row10 col2" >111.000000</td>
-      <td id="T_877ee_row10_col3" class="data row10 col3" >850.000000</td>
-      <td id="T_877ee_row10_col4" class="data row10 col4" >-0.099690</td>
+      <th id="T_85bcd_level0_row10" class="row_heading level0 row10" >Customer.NoOfDependents</th>
+      <td id="T_85bcd_row10_col0" class="data row10 col0" >0.0</td>
+      <td id="T_85bcd_row10_col1" class="data row10 col1" >1.000000</td>
+      <td id="T_85bcd_row10_col2" class="data row10 col2" >111.000000</td>
+      <td id="T_85bcd_row10_col3" class="data row10 col3" >850.000000</td>
+      <td id="T_85bcd_row10_col4" class="data row10 col4" >-0.099690</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row11" class="row_heading level0 row11" >Customer.Prefix</th>
-      <td id="T_877ee_row11_col0" class="data row11 col0" >Mrs.</td>
-      <td id="T_877ee_row11_col1" class="data row11 col1" >1.000000</td>
-      <td id="T_877ee_row11_col2" class="data row11 col2" >64.000000</td>
-      <td id="T_877ee_row11_col3" class="data row11 col3" >552.000000</td>
-      <td id="T_877ee_row11_col4" class="data row11 col4" >-0.216664</td>
+      <th id="T_85bcd_level0_row11" class="row_heading level0 row11" >Customer.Prefix</th>
+      <td id="T_85bcd_row11_col0" class="data row11 col0" >Mrs.</td>
+      <td id="T_85bcd_row11_col1" class="data row11 col1" >1.000000</td>
+      <td id="T_85bcd_row11_col2" class="data row11 col2" >64.000000</td>
+      <td id="T_85bcd_row11_col3" class="data row11 col3" >552.000000</td>
+      <td id="T_85bcd_row11_col4" class="data row11 col4" >-0.216664</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row12" class="row_heading level0 row12" >Customer.RelationshipStartDate</th>
-      <td id="T_877ee_row12_col0" class="data row12 col0" >1426.4596</td>
-      <td id="T_877ee_row12_col1" class="data row12 col1" >4.000000</td>
-      <td id="T_877ee_row12_col2" class="data row12 col2" >16.000000</td>
-      <td id="T_877ee_row12_col3" class="data row12 col3" >117.000000</td>
-      <td id="T_877ee_row12_col4" class="data row12 col4" >-0.050204</td>
+      <th id="T_85bcd_level0_row12" class="row_heading level0 row12" >Customer.RelationshipStartDate</th>
+      <td id="T_85bcd_row12_col0" class="data row12 col0" >1426.4596</td>
+      <td id="T_85bcd_row12_col1" class="data row12 col1" >4.000000</td>
+      <td id="T_85bcd_row12_col2" class="data row12 col2" >16.000000</td>
+      <td id="T_85bcd_row12_col3" class="data row12 col3" >117.000000</td>
+      <td id="T_85bcd_row12_col4" class="data row12 col4" >-0.050204</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row13" class="row_heading level0 row13" >Customer.RiskCode</th>
-      <td id="T_877ee_row13_col0" class="data row13 col0" >R4</td>
-      <td id="T_877ee_row13_col1" class="data row13 col1" >1.000000</td>
-      <td id="T_877ee_row13_col2" class="data row13 col2" >36.000000</td>
-      <td id="T_877ee_row13_col3" class="data row13 col3" >329.000000</td>
-      <td id="T_877ee_row13_col4" class="data row13 col4" >-0.270925</td>
+      <th id="T_85bcd_level0_row13" class="row_heading level0 row13" >Customer.RiskCode</th>
+      <td id="T_85bcd_row13_col0" class="data row13 col0" >R4</td>
+      <td id="T_85bcd_row13_col1" class="data row13 col1" >1.000000</td>
+      <td id="T_85bcd_row13_col2" class="data row13 col2" >36.000000</td>
+      <td id="T_85bcd_row13_col3" class="data row13 col3" >329.000000</td>
+      <td id="T_85bcd_row13_col4" class="data row13 col4" >-0.270925</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row14" class="row_heading level0 row14" >Customer.WinScore</th>
-      <td id="T_877ee_row14_col0" class="data row14 col0" >66.600006</td>
-      <td id="T_877ee_row14_col1" class="data row14 col1" >4.000000</td>
-      <td id="T_877ee_row14_col2" class="data row14 col2" >39.000000</td>
-      <td id="T_877ee_row14_col3" class="data row14 col3" >102.000000</td>
-      <td id="T_877ee_row14_col4" class="data row14 col4" >0.973755</td>
+      <th id="T_85bcd_level0_row14" class="row_heading level0 row14" >Customer.WinScore</th>
+      <td id="T_85bcd_row14_col0" class="data row14 col0" >66.600006</td>
+      <td id="T_85bcd_row14_col1" class="data row14 col1" >4.000000</td>
+      <td id="T_85bcd_row14_col2" class="data row14 col2" >39.000000</td>
+      <td id="T_85bcd_row14_col3" class="data row14 col3" >102.000000</td>
+      <td id="T_85bcd_row14_col4" class="data row14 col4" >0.973755</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row15" class="row_heading level0 row15" >Customer.pyCountry</th>
-      <td id="T_877ee_row15_col0" class="data row15 col0" >USA</td>
-      <td id="T_877ee_row15_col1" class="data row15 col1" >1.000000</td>
-      <td id="T_877ee_row15_col2" class="data row15 col2" >99.000000</td>
-      <td id="T_877ee_row15_col3" class="data row15 col3" >776.000000</td>
-      <td id="T_877ee_row15_col4" class="data row15 col4" >-0.122691</td>
+      <th id="T_85bcd_level0_row15" class="row_heading level0 row15" >Customer.pyCountry</th>
+      <td id="T_85bcd_row15_col0" class="data row15 col0" >USA</td>
+      <td id="T_85bcd_row15_col1" class="data row15 col1" >1.000000</td>
+      <td id="T_85bcd_row15_col2" class="data row15 col2" >99.000000</td>
+      <td id="T_85bcd_row15_col3" class="data row15 col3" >776.000000</td>
+      <td id="T_85bcd_row15_col4" class="data row15 col4" >-0.122691</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row16" class="row_heading level0 row16" >IH.Email.Outbound.Accepted.pxLastGroupID</th>
-      <td id="T_877ee_row16_col0" class="data row16 col0" >HomeLoans</td>
-      <td id="T_877ee_row16_col1" class="data row16 col1" >3.000000</td>
-      <td id="T_877ee_row16_col2" class="data row16 col2" >25.000000</td>
-      <td id="T_877ee_row16_col3" class="data row16 col3" >218.000000</td>
-      <td id="T_877ee_row16_col4" class="data row16 col4" >-0.227166</td>
+      <th id="T_85bcd_level0_row16" class="row_heading level0 row16" >IH.Email.Outbound.Accepted.pxLastGroupID</th>
+      <td id="T_85bcd_row16_col0" class="data row16 col0" >HomeLoans</td>
+      <td id="T_85bcd_row16_col1" class="data row16 col1" >3.000000</td>
+      <td id="T_85bcd_row16_col2" class="data row16 col2" >25.000000</td>
+      <td id="T_85bcd_row16_col3" class="data row16 col3" >218.000000</td>
+      <td id="T_85bcd_row16_col4" class="data row16 col4" >-0.227166</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row17" class="row_heading level0 row17" >IH.Email.Outbound.Accepted.pxLastOutcomeTime.DaysSince</th>
-      <td id="T_877ee_row17_col0" class="data row17 col0" >-55.88436</td>
-      <td id="T_877ee_row17_col1" class="data row17 col1" >2.000000</td>
-      <td id="T_877ee_row17_col2" class="data row17 col2" >145.000000</td>
-      <td id="T_877ee_row17_col3" class="data row17 col3" >881.000000</td>
-      <td id="T_877ee_row17_col4" class="data row17 col4" >0.130525</td>
+      <th id="T_85bcd_level0_row17" class="row_heading level0 row17" >IH.Email.Outbound.Accepted.pxLastOutcomeTime.DaysSince</th>
+      <td id="T_85bcd_row17_col0" class="data row17 col0" >-55.88436</td>
+      <td id="T_85bcd_row17_col1" class="data row17 col1" >2.000000</td>
+      <td id="T_85bcd_row17_col2" class="data row17 col2" >145.000000</td>
+      <td id="T_85bcd_row17_col3" class="data row17 col3" >881.000000</td>
+      <td id="T_85bcd_row17_col4" class="data row17 col4" >0.130525</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row18" class="row_heading level0 row18" >IH.Email.Outbound.Accepted.pyHistoricalOutcomeCount</th>
-      <td id="T_877ee_row18_col0" class="data row18 col0" >1.5</td>
-      <td id="T_877ee_row18_col1" class="data row18 col1" >2.000000</td>
-      <td id="T_877ee_row18_col2" class="data row18 col2" >30.000000</td>
-      <td id="T_877ee_row18_col3" class="data row18 col3" >351.000000</td>
-      <td id="T_877ee_row18_col4" class="data row18 col4" >-0.520104</td>
+      <th id="T_85bcd_level0_row18" class="row_heading level0 row18" >IH.Email.Outbound.Accepted.pyHistoricalOutcomeCount</th>
+      <td id="T_85bcd_row18_col0" class="data row18 col0" >1.5</td>
+      <td id="T_85bcd_row18_col1" class="data row18 col1" >2.000000</td>
+      <td id="T_85bcd_row18_col2" class="data row18 col2" >30.000000</td>
+      <td id="T_85bcd_row18_col3" class="data row18 col3" >351.000000</td>
+      <td id="T_85bcd_row18_col4" class="data row18 col4" >-0.520104</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row19" class="row_heading level0 row19" >IH.Email.Outbound.Churned.pyHistoricalOutcomeCount</th>
-      <td id="T_877ee_row19_col0" class="data row19 col0" >None</td>
-      <td id="T_877ee_row19_col1" class="data row19 col1" >1.000000</td>
-      <td id="T_877ee_row19_col2" class="data row19 col2" >143.000000</td>
-      <td id="T_877ee_row19_col3" class="data row19 col3" >898.000000</td>
-      <td id="T_877ee_row19_col4" class="data row19 col4" >0.101486</td>
+      <th id="T_85bcd_level0_row19" class="row_heading level0 row19" >IH.Email.Outbound.Churned.pyHistoricalOutcomeCount</th>
+      <td id="T_85bcd_row19_col0" class="data row19 col0" >None</td>
+      <td id="T_85bcd_row19_col1" class="data row19 col1" >1.000000</td>
+      <td id="T_85bcd_row19_col2" class="data row19 col2" >143.000000</td>
+      <td id="T_85bcd_row19_col3" class="data row19 col3" >898.000000</td>
+      <td id="T_85bcd_row19_col4" class="data row19 col4" >0.101486</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row20" class="row_heading level0 row20" >IH.Email.Outbound.Loyal.pxLastOutcomeTime.DaysSince</th>
-      <td id="T_877ee_row20_col0" class="data row20 col0" >None</td>
-      <td id="T_877ee_row20_col1" class="data row20 col1" >1.000000</td>
-      <td id="T_877ee_row20_col2" class="data row20 col2" >129.000000</td>
-      <td id="T_877ee_row20_col3" class="data row20 col3" >1071.000000</td>
-      <td id="T_877ee_row20_col4" class="data row20 col4" >-0.178813</td>
+      <th id="T_85bcd_level0_row20" class="row_heading level0 row20" >IH.Email.Outbound.Loyal.pxLastOutcomeTime.DaysSince</th>
+      <td id="T_85bcd_row20_col0" class="data row20 col0" >None</td>
+      <td id="T_85bcd_row20_col1" class="data row20 col1" >1.000000</td>
+      <td id="T_85bcd_row20_col2" class="data row20 col2" >129.000000</td>
+      <td id="T_85bcd_row20_col3" class="data row20 col3" >1071.000000</td>
+      <td id="T_85bcd_row20_col4" class="data row20 col4" >-0.178813</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row21" class="row_heading level0 row21" >IH.Email.Outbound.Rejected.pyHistoricalOutcomeCount</th>
-      <td id="T_877ee_row21_col0" class="data row21 col0" >83.16</td>
-      <td id="T_877ee_row21_col1" class="data row21 col1" >3.000000</td>
-      <td id="T_877ee_row21_col2" class="data row21 col2" >24.000000</td>
-      <td id="T_877ee_row21_col3" class="data row21 col3" >218.000000</td>
-      <td id="T_877ee_row21_col4" class="data row21 col4" >-0.267751</td>
+      <th id="T_85bcd_level0_row21" class="row_heading level0 row21" >IH.Email.Outbound.Rejected.pyHistoricalOutcomeCount</th>
+      <td id="T_85bcd_row21_col0" class="data row21 col0" >83.16</td>
+      <td id="T_85bcd_row21_col1" class="data row21 col1" >3.000000</td>
+      <td id="T_85bcd_row21_col2" class="data row21 col2" >24.000000</td>
+      <td id="T_85bcd_row21_col3" class="data row21 col3" >218.000000</td>
+      <td id="T_85bcd_row21_col4" class="data row21 col4" >-0.267751</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row22" class="row_heading level0 row22" >IH.SMS.Outbound.Accepted.pxLastGroupID</th>
-      <td id="T_877ee_row22_col0" class="data row22 col0" >Account</td>
-      <td id="T_877ee_row22_col1" class="data row22 col1" >4.000000</td>
-      <td id="T_877ee_row22_col2" class="data row22 col2" >45.000000</td>
-      <td id="T_877ee_row22_col3" class="data row22 col3" >316.000000</td>
-      <td id="T_877ee_row22_col4" class="data row22 col4" >-0.013291</td>
+      <th id="T_85bcd_level0_row22" class="row_heading level0 row22" >IH.SMS.Outbound.Accepted.pxLastGroupID</th>
+      <td id="T_85bcd_row22_col0" class="data row22 col0" >Account</td>
+      <td id="T_85bcd_row22_col1" class="data row22 col1" >4.000000</td>
+      <td id="T_85bcd_row22_col2" class="data row22 col2" >45.000000</td>
+      <td id="T_85bcd_row22_col3" class="data row22 col3" >316.000000</td>
+      <td id="T_85bcd_row22_col4" class="data row22 col4" >-0.013291</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row23" class="row_heading level0 row23" >IH.SMS.Outbound.Accepted.pyHistoricalOutcomeCount</th>
-      <td id="T_877ee_row23_col0" class="data row23 col0" >9.02</td>
-      <td id="T_877ee_row23_col1" class="data row23 col1" >4.000000</td>
-      <td id="T_877ee_row23_col2" class="data row23 col2" >6.000000</td>
-      <td id="T_877ee_row23_col3" class="data row23 col3" >96.000000</td>
-      <td id="T_877ee_row23_col4" class="data row23 col4" >-0.821986</td>
+      <th id="T_85bcd_level0_row23" class="row_heading level0 row23" >IH.SMS.Outbound.Accepted.pyHistoricalOutcomeCount</th>
+      <td id="T_85bcd_row23_col0" class="data row23 col0" >9.02</td>
+      <td id="T_85bcd_row23_col1" class="data row23 col1" >4.000000</td>
+      <td id="T_85bcd_row23_col2" class="data row23 col2" >6.000000</td>
+      <td id="T_85bcd_row23_col3" class="data row23 col3" >96.000000</td>
+      <td id="T_85bcd_row23_col4" class="data row23 col4" >-0.821986</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row24" class="row_heading level0 row24" >IH.SMS.Outbound.Churned.pxLastOutcomeTime.DaysSince</th>
-      <td id="T_877ee_row24_col0" class="data row24 col0" >-20.5537</td>
-      <td id="T_877ee_row24_col1" class="data row24 col1" >2.000000</td>
-      <td id="T_877ee_row24_col2" class="data row24 col2" >9.000000</td>
-      <td id="T_877ee_row24_col3" class="data row24 col3" >27.000000</td>
-      <td id="T_877ee_row24_col4" class="data row24 col4" >0.849243</td>
+      <th id="T_85bcd_level0_row24" class="row_heading level0 row24" >IH.SMS.Outbound.Churned.pxLastOutcomeTime.DaysSince</th>
+      <td id="T_85bcd_row24_col0" class="data row24 col0" >-20.5537</td>
+      <td id="T_85bcd_row24_col1" class="data row24 col1" >2.000000</td>
+      <td id="T_85bcd_row24_col2" class="data row24 col2" >9.000000</td>
+      <td id="T_85bcd_row24_col3" class="data row24 col3" >27.000000</td>
+      <td id="T_85bcd_row24_col4" class="data row24 col4" >0.849243</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row25" class="row_heading level0 row25" >IH.SMS.Outbound.Loyal.pxLastOutcomeTime.DaysSince</th>
-      <td id="T_877ee_row25_col0" class="data row25 col0" >None</td>
-      <td id="T_877ee_row25_col1" class="data row25 col1" >1.000000</td>
-      <td id="T_877ee_row25_col2" class="data row25 col2" >165.000000</td>
-      <td id="T_877ee_row25_col3" class="data row25 col3" >1240.000000</td>
-      <td id="T_877ee_row25_col4" class="data row25 col4" >-0.079718</td>
+      <th id="T_85bcd_level0_row25" class="row_heading level0 row25" >IH.SMS.Outbound.Loyal.pxLastOutcomeTime.DaysSince</th>
+      <td id="T_85bcd_row25_col0" class="data row25 col0" >None</td>
+      <td id="T_85bcd_row25_col1" class="data row25 col1" >1.000000</td>
+      <td id="T_85bcd_row25_col2" class="data row25 col2" >165.000000</td>
+      <td id="T_85bcd_row25_col3" class="data row25 col3" >1240.000000</td>
+      <td id="T_85bcd_row25_col4" class="data row25 col4" >-0.079718</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row26" class="row_heading level0 row26" >IH.SMS.Outbound.Loyal.pyHistoricalOutcomeCount</th>
-      <td id="T_877ee_row26_col0" class="data row26 col0" >None</td>
-      <td id="T_877ee_row26_col1" class="data row26 col1" >1.000000</td>
-      <td id="T_877ee_row26_col2" class="data row26 col2" >165.000000</td>
-      <td id="T_877ee_row26_col3" class="data row26 col3" >1240.000000</td>
-      <td id="T_877ee_row26_col4" class="data row26 col4" >-0.079718</td>
+      <th id="T_85bcd_level0_row26" class="row_heading level0 row26" >IH.SMS.Outbound.Loyal.pyHistoricalOutcomeCount</th>
+      <td id="T_85bcd_row26_col0" class="data row26 col0" >None</td>
+      <td id="T_85bcd_row26_col1" class="data row26 col1" >1.000000</td>
+      <td id="T_85bcd_row26_col2" class="data row26 col2" >165.000000</td>
+      <td id="T_85bcd_row26_col3" class="data row26 col3" >1240.000000</td>
+      <td id="T_85bcd_row26_col4" class="data row26 col4" >-0.079718</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row27" class="row_heading level0 row27" >IH.SMS.Outbound.Rejected.pxLastGroupID</th>
-      <td id="T_877ee_row27_col0" class="data row27 col0" >Account</td>
-      <td id="T_877ee_row27_col1" class="data row27 col1" >2.000000</td>
-      <td id="T_877ee_row27_col2" class="data row27 col2" >47.000000</td>
-      <td id="T_877ee_row27_col3" class="data row27 col3" >357.000000</td>
-      <td id="T_877ee_row27_col4" class="data row27 col4" >-0.090492</td>
+      <th id="T_85bcd_level0_row27" class="row_heading level0 row27" >IH.SMS.Outbound.Rejected.pxLastGroupID</th>
+      <td id="T_85bcd_row27_col0" class="data row27 col0" >Account</td>
+      <td id="T_85bcd_row27_col1" class="data row27 col1" >2.000000</td>
+      <td id="T_85bcd_row27_col2" class="data row27 col2" >47.000000</td>
+      <td id="T_85bcd_row27_col3" class="data row27 col3" >357.000000</td>
+      <td id="T_85bcd_row27_col4" class="data row27 col4" >-0.090492</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row28" class="row_heading level0 row28" >IH.SMS.Outbound.Rejected.pyHistoricalOutcomeCount</th>
-      <td id="T_877ee_row28_col0" class="data row28 col0" >102.72</td>
-      <td id="T_877ee_row28_col1" class="data row28 col1" >4.000000</td>
-      <td id="T_877ee_row28_col2" class="data row28 col2" >12.000000</td>
-      <td id="T_877ee_row28_col3" class="data row28 col3" >117.000000</td>
-      <td id="T_877ee_row28_col4" class="data row28 col4" >-0.335590</td>
+      <th id="T_85bcd_level0_row28" class="row_heading level0 row28" >IH.SMS.Outbound.Rejected.pyHistoricalOutcomeCount</th>
+      <td id="T_85bcd_row28_col0" class="data row28 col0" >102.72</td>
+      <td id="T_85bcd_row28_col1" class="data row28 col1" >4.000000</td>
+      <td id="T_85bcd_row28_col2" class="data row28 col2" >12.000000</td>
+      <td id="T_85bcd_row28_col3" class="data row28 col3" >117.000000</td>
+      <td id="T_85bcd_row28_col4" class="data row28 col4" >-0.335590</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row29" class="row_heading level0 row29" >IH.Web.Inbound.Accepted.pxLastGroupID</th>
-      <td id="T_877ee_row29_col0" class="data row29 col0" >DepositAccounts</td>
-      <td id="T_877ee_row29_col1" class="data row29 col1" >3.000000</td>
-      <td id="T_877ee_row29_col2" class="data row29 col2" >53.000000</td>
-      <td id="T_877ee_row29_col3" class="data row29 col3" >397.000000</td>
-      <td id="T_877ee_row29_col4" class="data row29 col4" >-0.077902</td>
+      <th id="T_85bcd_level0_row29" class="row_heading level0 row29" >IH.Web.Inbound.Accepted.pxLastGroupID</th>
+      <td id="T_85bcd_row29_col0" class="data row29 col0" >DepositAccounts</td>
+      <td id="T_85bcd_row29_col1" class="data row29 col1" >3.000000</td>
+      <td id="T_85bcd_row29_col2" class="data row29 col2" >53.000000</td>
+      <td id="T_85bcd_row29_col3" class="data row29 col3" >397.000000</td>
+      <td id="T_85bcd_row29_col4" class="data row29 col4" >-0.077902</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row30" class="row_heading level0 row30" >IH.Web.Inbound.Accepted.pyHistoricalOutcomeCount</th>
-      <td id="T_877ee_row30_col0" class="data row30 col0" >11.04</td>
-      <td id="T_877ee_row30_col1" class="data row30 col1" >5.000000</td>
-      <td id="T_877ee_row30_col2" class="data row30 col2" >25.000000</td>
-      <td id="T_877ee_row30_col3" class="data row30 col3" >164.000000</td>
-      <td id="T_877ee_row30_col4" class="data row30 col4" >0.055802</td>
+      <th id="T_85bcd_level0_row30" class="row_heading level0 row30" >IH.Web.Inbound.Accepted.pyHistoricalOutcomeCount</th>
+      <td id="T_85bcd_row30_col0" class="data row30 col0" >11.04</td>
+      <td id="T_85bcd_row30_col1" class="data row30 col1" >5.000000</td>
+      <td id="T_85bcd_row30_col2" class="data row30 col2" >25.000000</td>
+      <td id="T_85bcd_row30_col3" class="data row30 col3" >164.000000</td>
+      <td id="T_85bcd_row30_col4" class="data row30 col4" >0.055802</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row31" class="row_heading level0 row31" >IH.Web.Inbound.Loyal.pxLastGroupID</th>
-      <td id="T_877ee_row31_col0" class="data row31 col0" >MISSING</td>
-      <td id="T_877ee_row31_col1" class="data row31 col1" >1.000000</td>
-      <td id="T_877ee_row31_col2" class="data row31 col2" >100.000000</td>
-      <td id="T_877ee_row31_col3" class="data row31 col3" >857.000000</td>
-      <td id="T_877ee_row31_col4" class="data row31 col4" >-0.211919</td>
+      <th id="T_85bcd_level0_row31" class="row_heading level0 row31" >IH.Web.Inbound.Loyal.pxLastGroupID</th>
+      <td id="T_85bcd_row31_col0" class="data row31 col0" >MISSING</td>
+      <td id="T_85bcd_row31_col1" class="data row31 col1" >1.000000</td>
+      <td id="T_85bcd_row31_col2" class="data row31 col2" >100.000000</td>
+      <td id="T_85bcd_row31_col3" class="data row31 col3" >857.000000</td>
+      <td id="T_85bcd_row31_col4" class="data row31 col4" >-0.211919</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row32" class="row_heading level0 row32" >IH.Web.Inbound.Loyal.pyHistoricalOutcomeCount</th>
-      <td id="T_877ee_row32_col0" class="data row32 col0" >4.52</td>
-      <td id="T_877ee_row32_col1" class="data row32 col1" >3.000000</td>
-      <td id="T_877ee_row32_col2" class="data row32 col2" >30.000000</td>
-      <td id="T_877ee_row32_col3" class="data row32 col3" >212.000000</td>
-      <td id="T_877ee_row32_col4" class="data row32 col4" >-0.017224</td>
+      <th id="T_85bcd_level0_row32" class="row_heading level0 row32" >IH.Web.Inbound.Loyal.pyHistoricalOutcomeCount</th>
+      <td id="T_85bcd_row32_col0" class="data row32 col0" >4.52</td>
+      <td id="T_85bcd_row32_col1" class="data row32 col1" >3.000000</td>
+      <td id="T_85bcd_row32_col2" class="data row32 col2" >30.000000</td>
+      <td id="T_85bcd_row32_col3" class="data row32 col3" >212.000000</td>
+      <td id="T_85bcd_row32_col4" class="data row32 col4" >-0.017224</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row33" class="row_heading level0 row33" >IH.Web.Inbound.Rejected.pxLastGroupID</th>
-      <td id="T_877ee_row33_col0" class="data row33 col0" >Account</td>
-      <td id="T_877ee_row33_col1" class="data row33 col1" >2.000000</td>
-      <td id="T_877ee_row33_col2" class="data row33 col2" >81.000000</td>
-      <td id="T_877ee_row33_col3" class="data row33 col3" >546.000000</td>
-      <td id="T_877ee_row33_col4" class="data row33 col4" >0.027864</td>
+      <th id="T_85bcd_level0_row33" class="row_heading level0 row33" >IH.Web.Inbound.Rejected.pxLastGroupID</th>
+      <td id="T_85bcd_row33_col0" class="data row33 col0" >Account</td>
+      <td id="T_85bcd_row33_col1" class="data row33 col1" >2.000000</td>
+      <td id="T_85bcd_row33_col2" class="data row33 col2" >81.000000</td>
+      <td id="T_85bcd_row33_col3" class="data row33 col3" >546.000000</td>
+      <td id="T_85bcd_row33_col4" class="data row33 col4" >0.027864</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row34" class="row_heading level0 row34" >IH.Web.Inbound.Rejected.pyHistoricalOutcomeCount</th>
-      <td id="T_877ee_row34_col0" class="data row34 col0" >111.08</td>
-      <td id="T_877ee_row34_col1" class="data row34 col1" >4.000000</td>
-      <td id="T_877ee_row34_col2" class="data row34 col2" >35.000000</td>
-      <td id="T_877ee_row34_col3" class="data row34 col3" >306.000000</td>
-      <td id="T_877ee_row34_col4" class="data row34 col4" >-0.231670</td>
+      <th id="T_85bcd_level0_row34" class="row_heading level0 row34" >IH.Web.Inbound.Rejected.pyHistoricalOutcomeCount</th>
+      <td id="T_85bcd_row34_col0" class="data row34 col0" >111.08</td>
+      <td id="T_85bcd_row34_col1" class="data row34 col1" >4.000000</td>
+      <td id="T_85bcd_row34_col2" class="data row34 col2" >35.000000</td>
+      <td id="T_85bcd_row34_col3" class="data row34 col3" >306.000000</td>
+      <td id="T_85bcd_row34_col4" class="data row34 col4" >-0.231670</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row35" class="row_heading level0 row35" >Param.ExtGroupCreditcards</th>
-      <td id="T_877ee_row35_col0" class="data row35 col0" >NON-MISSING</td>
-      <td id="T_877ee_row35_col1" class="data row35 col1" >1.000000</td>
-      <td id="T_877ee_row35_col2" class="data row35 col2" >136.000000</td>
-      <td id="T_877ee_row35_col3" class="data row35 col3" >721.000000</td>
-      <td id="T_877ee_row35_col4" class="data row35 col4" >0.268402</td>
+      <th id="T_85bcd_level0_row35" class="row_heading level0 row35" >Param.ExtGroupCreditcards</th>
+      <td id="T_85bcd_row35_col0" class="data row35 col0" >NON-MISSING</td>
+      <td id="T_85bcd_row35_col1" class="data row35 col1" >1.000000</td>
+      <td id="T_85bcd_row35_col2" class="data row35 col2" >136.000000</td>
+      <td id="T_85bcd_row35_col3" class="data row35 col3" >721.000000</td>
+      <td id="T_85bcd_row35_col4" class="data row35 col4" >0.268402</td>
     </tr>
     <tr>
-      <th id="T_877ee_level0_row36" class="row_heading level0 row36" >Final Score</th>
-      <td id="T_877ee_row36_col0" class="data row36 col0" >None</td>
-      <td id="T_877ee_row36_col1" class="data row36 col1" >nan</td>
-      <td id="T_877ee_row36_col2" class="data row36 col2" >nan</td>
-      <td id="T_877ee_row36_col3" class="data row36 col3" >nan</td>
-      <td id="T_877ee_row36_col4" class="data row36 col4" >-0.149329</td>
+      <th id="T_85bcd_level0_row36" class="row_heading level0 row36" >Final Score</th>
+      <td id="T_85bcd_row36_col0" class="data row36 col0" >None</td>
+      <td id="T_85bcd_row36_col1" class="data row36 col1" >nan</td>
+      <td id="T_85bcd_row36_col2" class="data row36 col2" >nan</td>
+      <td id="T_85bcd_row36_col3" class="data row36 col3" >nan</td>
+      <td id="T_85bcd_row36_col4" class="data row36 col4" >-0.149329</td>
     </tr>
   </tbody>
 </table></div>
@@ -1713,23 +1713,23 @@ <h2>Classifier<a class="headerlink" href="#Classifier" title="Link to this headi
 </div>
 <div class="output_area rendered_html docutils container">
 <style type="text/css">
-#T_348bb_row0_col5, #T_348bb_row1_col5, #T_348bb_row2_col5, #T_348bb_row3_col5, #T_348bb_row4_col5, #T_348bb_row5_col5, #T_348bb_row6_col5, #T_348bb_row7_col5, #T_348bb_row8_col5, #T_348bb_row9_col5, #T_348bb_row10_col5, #T_348bb_row11_col5, #T_348bb_row12_col5, #T_348bb_row13_col5, #T_348bb_row14_col5 {
+#T_11d2d_row0_col5, #T_11d2d_row1_col5, #T_11d2d_row2_col5, #T_11d2d_row3_col5, #T_11d2d_row4_col5, #T_11d2d_row5_col5, #T_11d2d_row6_col5, #T_11d2d_row7_col5, #T_11d2d_row8_col5, #T_11d2d_row9_col5, #T_11d2d_row10_col5, #T_11d2d_row11_col5, #T_11d2d_row12_col5, #T_11d2d_row13_col5, #T_11d2d_row14_col5 {
   color: blue;
 }
 </style>
-<table id="T_348bb">
+<table id="T_11d2d">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_348bb_level0_col0" class="col_heading level0 col0" >Bin</th>
-      <th id="T_348bb_level0_col1" class="col_heading level0 col1" >Positives</th>
-      <th id="T_348bb_level0_col2" class="col_heading level0 col2" >Negatives</th>
-      <th id="T_348bb_level0_col3" class="col_heading level0 col3" >Cum. Total (%)</th>
-      <th id="T_348bb_level0_col4" class="col_heading level0 col4" >Propensity (%)</th>
-      <th id="T_348bb_level0_col5" class="col_heading level0 col5" >Adjusted Propensity (%)</th>
-      <th id="T_348bb_level0_col6" class="col_heading level0 col6" >Cum Positives (%)</th>
-      <th id="T_348bb_level0_col7" class="col_heading level0 col7" >ZRatio</th>
-      <th id="T_348bb_level0_col8" class="col_heading level0 col8" >Lift(%)</th>
+      <th id="T_11d2d_level0_col0" class="col_heading level0 col0" >Bin</th>
+      <th id="T_11d2d_level0_col1" class="col_heading level0 col1" >Positives</th>
+      <th id="T_11d2d_level0_col2" class="col_heading level0 col2" >Negatives</th>
+      <th id="T_11d2d_level0_col3" class="col_heading level0 col3" >Cum. Total (%)</th>
+      <th id="T_11d2d_level0_col4" class="col_heading level0 col4" >Propensity (%)</th>
+      <th id="T_11d2d_level0_col5" class="col_heading level0 col5" >Adjusted Propensity (%)</th>
+      <th id="T_11d2d_level0_col6" class="col_heading level0 col6" >Cum Positives (%)</th>
+      <th id="T_11d2d_level0_col7" class="col_heading level0 col7" >ZRatio</th>
+      <th id="T_11d2d_level0_col8" class="col_heading level0 col8" >Lift(%)</th>
     </tr>
     <tr>
       <th class="index_name level0" >Index</th>
@@ -1746,184 +1746,184 @@ <h2>Classifier<a class="headerlink" href="#Classifier" title="Link to this headi
   </thead>
   <tbody>
     <tr>
-      <th id="T_348bb_level0_row0" class="row_heading level0 row0" >1</th>
-      <td id="T_348bb_row0_col0" class="data row0 col0" ><-0.21</td>
-      <td id="T_348bb_row0_col1" class="data row0 col1" >17</td>
-      <td id="T_348bb_row0_col2" class="data row0 col2" >443</td>
-      <td id="T_348bb_row0_col3" class="data row0 col3" >28.117361</td>
-      <td id="T_348bb_row0_col4" class="data row0 col4" >3.695652</td>
-      <td id="T_348bb_row0_col5" class="data row0 col5" >3.796095</td>
-      <td id="T_348bb_row0_col6" class="data row0 col6" >8.252427</td>
-      <td id="T_348bb_row0_col7" class="data row0 col7" >-9.994484</td>
-      <td id="T_348bb_row0_col8" class="data row0 col8" >1.956662</td>
-    </tr>
-    <tr>
-      <th id="T_348bb_level0_row1" class="row_heading level0 row1" >2</th>
-      <td id="T_348bb_row1_col0" class="data row1 col0" >[-0.21, -0.185></td>
-      <td id="T_348bb_row1_col1" class="data row1 col1" >8</td>
-      <td id="T_348bb_row1_col2" class="data row1 col2" >133</td>
-      <td id="T_348bb_row1_col3" class="data row1 col3" >36.735943</td>
-      <td id="T_348bb_row1_col4" class="data row1 col4" >5.673759</td>
-      <td id="T_348bb_row1_col5" class="data row1 col5" >5.985916</td>
-      <td id="T_348bb_row1_col6" class="data row1 col6" >12.135922</td>
-      <td id="T_348bb_row1_col7" class="data row1 col7" >-3.495416</td>
-      <td id="T_348bb_row1_col8" class="data row1 col8" >3.003971</td>
-    </tr>
-    <tr>
-      <th id="T_348bb_level0_row2" class="row_heading level0 row2" >3</th>
-      <td id="T_348bb_row2_col0" class="data row2 col0" >[-0.185, -0.175></td>
-      <td id="T_348bb_row2_col1" class="data row2 col1" >3</td>
-      <td id="T_348bb_row2_col2" class="data row2 col2" >48</td>
-      <td id="T_348bb_row2_col3" class="data row2 col3" >39.853302</td>
-      <td id="T_348bb_row2_col4" class="data row2 col4" >5.882353</td>
-      <td id="T_348bb_row2_col5" class="data row2 col5" >6.730770</td>
-      <td id="T_348bb_row2_col6" class="data row2 col6" >13.592233</td>
-      <td id="T_348bb_row2_col7" class="data row2 col7" >-1.977473</td>
-      <td id="T_348bb_row2_col8" class="data row2 col8" >3.114411</td>
-    </tr>
-    <tr>
-      <th id="T_348bb_level0_row3" class="row_heading level0 row3" >4</th>
-      <td id="T_348bb_row3_col0" class="data row3 col0" >[-0.175, -0.105></td>
-      <td id="T_348bb_row3_col1" class="data row3 col1" >28</td>
-      <td id="T_348bb_row3_col2" class="data row3 col2" >370</td>
-      <td id="T_348bb_row3_col3" class="data row3 col3" >64.180931</td>
-      <td id="T_348bb_row3_col4" class="data row3 col4" >7.035176</td>
-      <td id="T_348bb_row3_col5" class="data row3 col5" >7.142858</td>
-      <td id="T_348bb_row3_col6" class="data row3 col6" >27.184465</td>
-      <td id="T_348bb_row3_col7" class="data row3 col7" >-4.628075</td>
-      <td id="T_348bb_row3_col8" class="data row3 col8" >3.724772</td>
-    </tr>
-    <tr>
-      <th id="T_348bb_level0_row4" class="row_heading level0 row4" >5</th>
-      <td id="T_348bb_row4_col0" class="data row4 col0" >[-0.105, -0.095></td>
-      <td id="T_348bb_row4_col1" class="data row4 col1" >4</td>
-      <td id="T_348bb_row4_col2" class="data row4 col2" >51</td>
-      <td id="T_348bb_row4_col3" class="data row4 col3" >67.542793</td>
-      <td id="T_348bb_row4_col4" class="data row4 col4" >7.272727</td>
-      <td id="T_348bb_row4_col5" class="data row4 col5" >8.035714</td>
-      <td id="T_348bb_row4_col6" class="data row4 col6" >29.126215</td>
-      <td id="T_348bb_row4_col7" class="data row4 col7" >-1.505372</td>
-      <td id="T_348bb_row4_col8" class="data row4 col8" >3.850544</td>
-    </tr>
-    <tr>
-      <th id="T_348bb_level0_row5" class="row_heading level0 row5" >6</th>
-      <td id="T_348bb_row5_col0" class="data row5 col0" >[-0.095, -0.09></td>
-      <td id="T_348bb_row5_col1" class="data row5 col1" >2</td>
-      <td id="T_348bb_row5_col2" class="data row5 col2" >19</td>
-      <td id="T_348bb_row5_col3" class="data row5 col3" >68.826408</td>
-      <td id="T_348bb_row5_col4" class="data row5 col4" >9.523809</td>
-      <td id="T_348bb_row5_col5" class="data row5 col5" >11.363637</td>
-      <td id="T_348bb_row5_col6" class="data row5 col6" >30.097088</td>
-      <td id="T_348bb_row5_col7" class="data row5 col7" >-0.478811</td>
-      <td id="T_348bb_row5_col8" class="data row5 col8" >5.042379</td>
-    </tr>
-    <tr>
-      <th id="T_348bb_level0_row6" class="row_heading level0 row6" >7</th>
-      <td id="T_348bb_row6_col0" class="data row6 col0" >[-0.09, -0.065></td>
-      <td id="T_348bb_row6_col1" class="data row6 col1" >9</td>
-      <td id="T_348bb_row6_col2" class="data row6 col2" >77</td>
-      <td id="T_348bb_row6_col3" class="data row6 col3" >74.083130</td>
-      <td id="T_348bb_row6_col4" class="data row6 col4" >10.465117</td>
-      <td id="T_348bb_row6_col5" class="data row6 col5" >10.919540</td>
-      <td id="T_348bb_row6_col6" class="data row6 col6" >34.466019</td>
-      <td id="T_348bb_row6_col7" class="data row6 col7" >-0.657755</td>
-      <td id="T_348bb_row6_col8" class="data row6 col8" >5.540754</td>
-    </tr>
-    <tr>
-      <th id="T_348bb_level0_row7" class="row_heading level0 row7" >8</th>
-      <td id="T_348bb_row7_col0" class="data row7 col0" >[-0.065, -0.02></td>
-      <td id="T_348bb_row7_col1" class="data row7 col1" >30</td>
-      <td id="T_348bb_row7_col2" class="data row7 col2" >154</td>
-      <td id="T_348bb_row7_col3" class="data row7 col3" >85.330078</td>
-      <td id="T_348bb_row7_col4" class="data row7 col4" >16.304348</td>
-      <td id="T_348bb_row7_col5" class="data row7 col5" >16.486486</td>
-      <td id="T_348bb_row7_col6" class="data row7 col6" >49.029125</td>
-      <td id="T_348bb_row7_col7" class="data row7 col7" >1.464400</td>
-      <td id="T_348bb_row7_col8" class="data row7 col8" >8.632335</td>
-    </tr>
-    <tr>
-      <th id="T_348bb_level0_row8" class="row_heading level0 row8" >9</th>
-      <td id="T_348bb_row8_col0" class="data row8 col0" >[-0.02, 0.03></td>
-      <td id="T_348bb_row8_col1" class="data row8 col1" >37</td>
-      <td id="T_348bb_row8_col2" class="data row8 col2" >65</td>
-      <td id="T_348bb_row8_col3" class="data row8 col3" >91.564796</td>
-      <td id="T_348bb_row8_col4" class="data row8 col4" >36.274509</td>
-      <td id="T_348bb_row8_col5" class="data row8 col5" >36.407764</td>
-      <td id="T_348bb_row8_col6" class="data row8 col6" >66.990295</td>
-      <td id="T_348bb_row8_col7" class="data row8 col7" >4.913029</td>
-      <td id="T_348bb_row8_col8" class="data row8 col8" >19.205532</td>
-    </tr>
-    <tr>
-      <th id="T_348bb_level0_row9" class="row_heading level0 row9" >10</th>
-      <td id="T_348bb_row9_col0" class="data row9 col0" >[0.03, 0.06></td>
-      <td id="T_348bb_row9_col1" class="data row9 col1" >20</td>
-      <td id="T_348bb_row9_col2" class="data row9 col2" >29</td>
-      <td id="T_348bb_row9_col3" class="data row9 col3" >94.559906</td>
-      <td id="T_348bb_row9_col4" class="data row9 col4" >40.816326</td>
-      <td id="T_348bb_row9_col5" class="data row9 col5" >41.000000</td>
-      <td id="T_348bb_row9_col6" class="data row9 col6" >76.699028</td>
-      <td id="T_348bb_row9_col7" class="data row9 col7" >3.664015</td>
-      <td id="T_348bb_row9_col8" class="data row9 col8" >21.610197</td>
-    </tr>
-    <tr>
-      <th id="T_348bb_level0_row10" class="row_heading level0 row10" >11</th>
-      <td id="T_348bb_row10_col0" class="data row10 col0" >[0.06, 0.12></td>
-      <td id="T_348bb_row10_col1" class="data row10 col1" >30</td>
-      <td id="T_348bb_row10_col2" class="data row10 col2" >33</td>
-      <td id="T_348bb_row10_col3" class="data row10 col3" >98.410759</td>
-      <td id="T_348bb_row10_col4" class="data row10 col4" >47.619049</td>
-      <td id="T_348bb_row10_col5" class="data row10 col5" >47.656250</td>
-      <td id="T_348bb_row10_col6" class="data row10 col6" >91.262138</td>
-      <td id="T_348bb_row10_col7" class="data row10 col7" >4.922851</td>
-      <td id="T_348bb_row10_col8" class="data row10 col8" >25.211897</td>
-    </tr>
-    <tr>
-      <th id="T_348bb_level0_row11" class="row_heading level0 row11" >12</th>
-      <td id="T_348bb_row11_col0" class="data row11 col0" >[0.12, 0.125></td>
-      <td id="T_348bb_row11_col1" class="data row11 col1" >2</td>
-      <td id="T_348bb_row11_col2" class="data row11 col2" >2</td>
-      <td id="T_348bb_row11_col3" class="data row11 col3" >98.655258</td>
-      <td id="T_348bb_row11_col4" class="data row11 col4" >50.000000</td>
-      <td id="T_348bb_row11_col5" class="data row11 col5" >50.000000</td>
-      <td id="T_348bb_row11_col6" class="data row11 col6" >92.233009</td>
-      <td id="T_348bb_row11_col7" class="data row11 col7" >1.203876</td>
-      <td id="T_348bb_row11_col8" class="data row11 col8" >26.472490</td>
-    </tr>
-    <tr>
-      <th id="T_348bb_level0_row12" class="row_heading level0 row12" >13</th>
-      <td id="T_348bb_row12_col0" class="data row12 col0" >[0.125, 0.13></td>
-      <td id="T_348bb_row12_col1" class="data row12 col1" >4</td>
-      <td id="T_348bb_row12_col2" class="data row12 col2" >2</td>
-      <td id="T_348bb_row12_col3" class="data row12 col3" >99.022003</td>
-      <td id="T_348bb_row12_col4" class="data row12 col4" >66.666672</td>
-      <td id="T_348bb_row12_col5" class="data row12 col5" >64.285713</td>
-      <td id="T_348bb_row12_col6" class="data row12 col6" >94.174759</td>
-      <td id="T_348bb_row12_col7" class="data row12 col7" >1.864405</td>
-      <td id="T_348bb_row12_col8" class="data row12 col8" >35.296654</td>
-    </tr>
-    <tr>
-      <th id="T_348bb_level0_row13" class="row_heading level0 row13" >14</th>
-      <td id="T_348bb_row13_col0" class="data row13 col0" >[0.13, 0.995></td>
-      <td id="T_348bb_row13_col1" class="data row13 col1" >8</td>
-      <td id="T_348bb_row13_col2" class="data row13 col2" >3</td>
-      <td id="T_348bb_row13_col3" class="data row13 col3" >99.694382</td>
-      <td id="T_348bb_row13_col4" class="data row13 col4" >72.727272</td>
-      <td id="T_348bb_row13_col5" class="data row13 col5" >70.833328</td>
-      <td id="T_348bb_row13_col6" class="data row13 col6" >98.058250</td>
-      <td id="T_348bb_row13_col7" class="data row13 col7" >2.718192</td>
-      <td id="T_348bb_row13_col8" class="data row13 col8" >38.505444</td>
-    </tr>
-    <tr>
-      <th id="T_348bb_level0_row14" class="row_heading level0 row14" >15</th>
-      <td id="T_348bb_row14_col0" class="data row14 col0" >>=0.995</td>
-      <td id="T_348bb_row14_col1" class="data row14 col1" >4</td>
-      <td id="T_348bb_row14_col2" class="data row14 col2" >1</td>
-      <td id="T_348bb_row14_col3" class="data row14 col3" >100.000000</td>
-      <td id="T_348bb_row14_col4" class="data row14 col4" >80.000000</td>
-      <td id="T_348bb_row14_col5" class="data row14 col5" >75.000000</td>
-      <td id="T_348bb_row14_col6" class="data row14 col6" >100.000000</td>
-      <td id="T_348bb_row14_col7" class="data row14 col7" >1.941841</td>
-      <td id="T_348bb_row14_col8" class="data row14 col8" >42.355988</td>
+      <th id="T_11d2d_level0_row0" class="row_heading level0 row0" >1</th>
+      <td id="T_11d2d_row0_col0" class="data row0 col0" ><-0.21</td>
+      <td id="T_11d2d_row0_col1" class="data row0 col1" >17</td>
+      <td id="T_11d2d_row0_col2" class="data row0 col2" >443</td>
+      <td id="T_11d2d_row0_col3" class="data row0 col3" >28.117361</td>
+      <td id="T_11d2d_row0_col4" class="data row0 col4" >3.695652</td>
+      <td id="T_11d2d_row0_col5" class="data row0 col5" >3.796095</td>
+      <td id="T_11d2d_row0_col6" class="data row0 col6" >8.252427</td>
+      <td id="T_11d2d_row0_col7" class="data row0 col7" >-9.994484</td>
+      <td id="T_11d2d_row0_col8" class="data row0 col8" >1.956662</td>
+    </tr>
+    <tr>
+      <th id="T_11d2d_level0_row1" class="row_heading level0 row1" >2</th>
+      <td id="T_11d2d_row1_col0" class="data row1 col0" >[-0.21, -0.185></td>
+      <td id="T_11d2d_row1_col1" class="data row1 col1" >8</td>
+      <td id="T_11d2d_row1_col2" class="data row1 col2" >133</td>
+      <td id="T_11d2d_row1_col3" class="data row1 col3" >36.735943</td>
+      <td id="T_11d2d_row1_col4" class="data row1 col4" >5.673759</td>
+      <td id="T_11d2d_row1_col5" class="data row1 col5" >5.985916</td>
+      <td id="T_11d2d_row1_col6" class="data row1 col6" >12.135922</td>
+      <td id="T_11d2d_row1_col7" class="data row1 col7" >-3.495416</td>
+      <td id="T_11d2d_row1_col8" class="data row1 col8" >3.003971</td>
+    </tr>
+    <tr>
+      <th id="T_11d2d_level0_row2" class="row_heading level0 row2" >3</th>
+      <td id="T_11d2d_row2_col0" class="data row2 col0" >[-0.185, -0.175></td>
+      <td id="T_11d2d_row2_col1" class="data row2 col1" >3</td>
+      <td id="T_11d2d_row2_col2" class="data row2 col2" >48</td>
+      <td id="T_11d2d_row2_col3" class="data row2 col3" >39.853302</td>
+      <td id="T_11d2d_row2_col4" class="data row2 col4" >5.882353</td>
+      <td id="T_11d2d_row2_col5" class="data row2 col5" >6.730770</td>
+      <td id="T_11d2d_row2_col6" class="data row2 col6" >13.592233</td>
+      <td id="T_11d2d_row2_col7" class="data row2 col7" >-1.977473</td>
+      <td id="T_11d2d_row2_col8" class="data row2 col8" >3.114411</td>
+    </tr>
+    <tr>
+      <th id="T_11d2d_level0_row3" class="row_heading level0 row3" >4</th>
+      <td id="T_11d2d_row3_col0" class="data row3 col0" >[-0.175, -0.105></td>
+      <td id="T_11d2d_row3_col1" class="data row3 col1" >28</td>
+      <td id="T_11d2d_row3_col2" class="data row3 col2" >370</td>
+      <td id="T_11d2d_row3_col3" class="data row3 col3" >64.180931</td>
+      <td id="T_11d2d_row3_col4" class="data row3 col4" >7.035176</td>
+      <td id="T_11d2d_row3_col5" class="data row3 col5" >7.142858</td>
+      <td id="T_11d2d_row3_col6" class="data row3 col6" >27.184465</td>
+      <td id="T_11d2d_row3_col7" class="data row3 col7" >-4.628075</td>
+      <td id="T_11d2d_row3_col8" class="data row3 col8" >3.724772</td>
+    </tr>
+    <tr>
+      <th id="T_11d2d_level0_row4" class="row_heading level0 row4" >5</th>
+      <td id="T_11d2d_row4_col0" class="data row4 col0" >[-0.105, -0.095></td>
+      <td id="T_11d2d_row4_col1" class="data row4 col1" >4</td>
+      <td id="T_11d2d_row4_col2" class="data row4 col2" >51</td>
+      <td id="T_11d2d_row4_col3" class="data row4 col3" >67.542793</td>
+      <td id="T_11d2d_row4_col4" class="data row4 col4" >7.272727</td>
+      <td id="T_11d2d_row4_col5" class="data row4 col5" >8.035714</td>
+      <td id="T_11d2d_row4_col6" class="data row4 col6" >29.126215</td>
+      <td id="T_11d2d_row4_col7" class="data row4 col7" >-1.505372</td>
+      <td id="T_11d2d_row4_col8" class="data row4 col8" >3.850544</td>
+    </tr>
+    <tr>
+      <th id="T_11d2d_level0_row5" class="row_heading level0 row5" >6</th>
+      <td id="T_11d2d_row5_col0" class="data row5 col0" >[-0.095, -0.09></td>
+      <td id="T_11d2d_row5_col1" class="data row5 col1" >2</td>
+      <td id="T_11d2d_row5_col2" class="data row5 col2" >19</td>
+      <td id="T_11d2d_row5_col3" class="data row5 col3" >68.826408</td>
+      <td id="T_11d2d_row5_col4" class="data row5 col4" >9.523809</td>
+      <td id="T_11d2d_row5_col5" class="data row5 col5" >11.363637</td>
+      <td id="T_11d2d_row5_col6" class="data row5 col6" >30.097088</td>
+      <td id="T_11d2d_row5_col7" class="data row5 col7" >-0.478811</td>
+      <td id="T_11d2d_row5_col8" class="data row5 col8" >5.042379</td>
+    </tr>
+    <tr>
+      <th id="T_11d2d_level0_row6" class="row_heading level0 row6" >7</th>
+      <td id="T_11d2d_row6_col0" class="data row6 col0" >[-0.09, -0.065></td>
+      <td id="T_11d2d_row6_col1" class="data row6 col1" >9</td>
+      <td id="T_11d2d_row6_col2" class="data row6 col2" >77</td>
+      <td id="T_11d2d_row6_col3" class="data row6 col3" >74.083130</td>
+      <td id="T_11d2d_row6_col4" class="data row6 col4" >10.465117</td>
+      <td id="T_11d2d_row6_col5" class="data row6 col5" >10.919540</td>
+      <td id="T_11d2d_row6_col6" class="data row6 col6" >34.466019</td>
+      <td id="T_11d2d_row6_col7" class="data row6 col7" >-0.657755</td>
+      <td id="T_11d2d_row6_col8" class="data row6 col8" >5.540754</td>
+    </tr>
+    <tr>
+      <th id="T_11d2d_level0_row7" class="row_heading level0 row7" >8</th>
+      <td id="T_11d2d_row7_col0" class="data row7 col0" >[-0.065, -0.02></td>
+      <td id="T_11d2d_row7_col1" class="data row7 col1" >30</td>
+      <td id="T_11d2d_row7_col2" class="data row7 col2" >154</td>
+      <td id="T_11d2d_row7_col3" class="data row7 col3" >85.330078</td>
+      <td id="T_11d2d_row7_col4" class="data row7 col4" >16.304348</td>
+      <td id="T_11d2d_row7_col5" class="data row7 col5" >16.486486</td>
+      <td id="T_11d2d_row7_col6" class="data row7 col6" >49.029125</td>
+      <td id="T_11d2d_row7_col7" class="data row7 col7" >1.464400</td>
+      <td id="T_11d2d_row7_col8" class="data row7 col8" >8.632335</td>
+    </tr>
+    <tr>
+      <th id="T_11d2d_level0_row8" class="row_heading level0 row8" >9</th>
+      <td id="T_11d2d_row8_col0" class="data row8 col0" >[-0.02, 0.03></td>
+      <td id="T_11d2d_row8_col1" class="data row8 col1" >37</td>
+      <td id="T_11d2d_row8_col2" class="data row8 col2" >65</td>
+      <td id="T_11d2d_row8_col3" class="data row8 col3" >91.564796</td>
+      <td id="T_11d2d_row8_col4" class="data row8 col4" >36.274509</td>
+      <td id="T_11d2d_row8_col5" class="data row8 col5" >36.407764</td>
+      <td id="T_11d2d_row8_col6" class="data row8 col6" >66.990295</td>
+      <td id="T_11d2d_row8_col7" class="data row8 col7" >4.913029</td>
+      <td id="T_11d2d_row8_col8" class="data row8 col8" >19.205532</td>
+    </tr>
+    <tr>
+      <th id="T_11d2d_level0_row9" class="row_heading level0 row9" >10</th>
+      <td id="T_11d2d_row9_col0" class="data row9 col0" >[0.03, 0.06></td>
+      <td id="T_11d2d_row9_col1" class="data row9 col1" >20</td>
+      <td id="T_11d2d_row9_col2" class="data row9 col2" >29</td>
+      <td id="T_11d2d_row9_col3" class="data row9 col3" >94.559906</td>
+      <td id="T_11d2d_row9_col4" class="data row9 col4" >40.816326</td>
+      <td id="T_11d2d_row9_col5" class="data row9 col5" >41.000000</td>
+      <td id="T_11d2d_row9_col6" class="data row9 col6" >76.699028</td>
+      <td id="T_11d2d_row9_col7" class="data row9 col7" >3.664015</td>
+      <td id="T_11d2d_row9_col8" class="data row9 col8" >21.610197</td>
+    </tr>
+    <tr>
+      <th id="T_11d2d_level0_row10" class="row_heading level0 row10" >11</th>
+      <td id="T_11d2d_row10_col0" class="data row10 col0" >[0.06, 0.12></td>
+      <td id="T_11d2d_row10_col1" class="data row10 col1" >30</td>
+      <td id="T_11d2d_row10_col2" class="data row10 col2" >33</td>
+      <td id="T_11d2d_row10_col3" class="data row10 col3" >98.410759</td>
+      <td id="T_11d2d_row10_col4" class="data row10 col4" >47.619049</td>
+      <td id="T_11d2d_row10_col5" class="data row10 col5" >47.656250</td>
+      <td id="T_11d2d_row10_col6" class="data row10 col6" >91.262138</td>
+      <td id="T_11d2d_row10_col7" class="data row10 col7" >4.922851</td>
+      <td id="T_11d2d_row10_col8" class="data row10 col8" >25.211897</td>
+    </tr>
+    <tr>
+      <th id="T_11d2d_level0_row11" class="row_heading level0 row11" >12</th>
+      <td id="T_11d2d_row11_col0" class="data row11 col0" >[0.12, 0.125></td>
+      <td id="T_11d2d_row11_col1" class="data row11 col1" >2</td>
+      <td id="T_11d2d_row11_col2" class="data row11 col2" >2</td>
+      <td id="T_11d2d_row11_col3" class="data row11 col3" >98.655258</td>
+      <td id="T_11d2d_row11_col4" class="data row11 col4" >50.000000</td>
+      <td id="T_11d2d_row11_col5" class="data row11 col5" >50.000000</td>
+      <td id="T_11d2d_row11_col6" class="data row11 col6" >92.233009</td>
+      <td id="T_11d2d_row11_col7" class="data row11 col7" >1.203876</td>
+      <td id="T_11d2d_row11_col8" class="data row11 col8" >26.472490</td>
+    </tr>
+    <tr>
+      <th id="T_11d2d_level0_row12" class="row_heading level0 row12" >13</th>
+      <td id="T_11d2d_row12_col0" class="data row12 col0" >[0.125, 0.13></td>
+      <td id="T_11d2d_row12_col1" class="data row12 col1" >4</td>
+      <td id="T_11d2d_row12_col2" class="data row12 col2" >2</td>
+      <td id="T_11d2d_row12_col3" class="data row12 col3" >99.022003</td>
+      <td id="T_11d2d_row12_col4" class="data row12 col4" >66.666672</td>
+      <td id="T_11d2d_row12_col5" class="data row12 col5" >64.285713</td>
+      <td id="T_11d2d_row12_col6" class="data row12 col6" >94.174759</td>
+      <td id="T_11d2d_row12_col7" class="data row12 col7" >1.864405</td>
+      <td id="T_11d2d_row12_col8" class="data row12 col8" >35.296654</td>
+    </tr>
+    <tr>
+      <th id="T_11d2d_level0_row13" class="row_heading level0 row13" >14</th>
+      <td id="T_11d2d_row13_col0" class="data row13 col0" >[0.13, 0.995></td>
+      <td id="T_11d2d_row13_col1" class="data row13 col1" >8</td>
+      <td id="T_11d2d_row13_col2" class="data row13 col2" >3</td>
+      <td id="T_11d2d_row13_col3" class="data row13 col3" >99.694382</td>
+      <td id="T_11d2d_row13_col4" class="data row13 col4" >72.727272</td>
+      <td id="T_11d2d_row13_col5" class="data row13 col5" >70.833328</td>
+      <td id="T_11d2d_row13_col6" class="data row13 col6" >98.058250</td>
+      <td id="T_11d2d_row13_col7" class="data row13 col7" >2.718192</td>
+      <td id="T_11d2d_row13_col8" class="data row13 col8" >38.505444</td>
+    </tr>
+    <tr>
+      <th id="T_11d2d_level0_row14" class="row_heading level0 row14" >15</th>
+      <td id="T_11d2d_row14_col0" class="data row14 col0" >>=0.995</td>
+      <td id="T_11d2d_row14_col1" class="data row14 col1" >4</td>
+      <td id="T_11d2d_row14_col2" class="data row14 col2" >1</td>
+      <td id="T_11d2d_row14_col3" class="data row14 col3" >100.000000</td>
+      <td id="T_11d2d_row14_col4" class="data row14 col4" >80.000000</td>
+      <td id="T_11d2d_row14_col5" class="data row14 col5" >75.000000</td>
+      <td id="T_11d2d_row14_col6" class="data row14 col6" >100.000000</td>
+      <td id="T_11d2d_row14_col7" class="data row14 col7" >1.941841</td>
+      <td id="T_11d2d_row14_col8" class="data row14 col8" >42.355988</td>
     </tr>
   </tbody>
 </table></div>
@@ -1937,11 +1937,11 @@ <h2>Final Propensity<a class="headerlink" href="#Final-Propensity" title="Link t
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-/tmp/ipykernel_2994/2151495297.py:3: DeprecationWarning:
+/tmp/ipykernel_2930/2151495297.py:3: DeprecationWarning:
 
 `where` is deprecated. Use `filter` instead.
 
-/tmp/ipykernel_2994/2151495297.py:25: DeprecationWarning:
+/tmp/ipykernel_2930/2151495297.py:25: DeprecationWarning:
 
 `where` is deprecated. Use `filter` instead.
 
@@ -1951,9 +1951,9 @@ <h2>Final Propensity<a class="headerlink" href="#Final-Propensity" title="Link t
 <div class="prompt empty docutils container">
 </div>
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-var gd = document.getElementById('2c48085e-cce5-43ab-bd1c-20351b325ea8');
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diff --git a/Python/articles/ADMExplained.ipynb b/Python/articles/ADMExplained.ipynb
index 3372ab31..ac7fccf9 100644
--- a/Python/articles/ADMExplained.ipynb
+++ b/Python/articles/ADMExplained.ipynb
@@ -26,10 +26,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:32.446830Z",
-     "iopub.status.busy": "2024-06-27T08:57:32.446346Z",
-     "iopub.status.idle": "2024-06-27T08:57:32.793893Z",
-     "shell.execute_reply": "2024-06-27T08:57:32.793392Z"
+     "iopub.execute_input": "2024-06-28T08:59:08.826229Z",
+     "iopub.status.busy": "2024-06-28T08:59:08.826065Z",
+     "iopub.status.idle": "2024-06-28T08:59:09.183906Z",
+     "shell.execute_reply": "2024-06-28T08:59:09.183402Z"
     },
     "nbsphinx": "hidden"
    },
@@ -61,10 +61,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:32.796336Z",
-     "iopub.status.busy": "2024-06-27T08:57:32.795991Z",
-     "iopub.status.idle": "2024-06-27T08:57:33.585594Z",
-     "shell.execute_reply": "2024-06-27T08:57:33.584954Z"
+     "iopub.execute_input": "2024-06-28T08:59:09.186724Z",
+     "iopub.status.busy": "2024-06-28T08:59:09.186094Z",
+     "iopub.status.idle": "2024-06-28T08:59:09.982196Z",
+     "shell.execute_reply": "2024-06-28T08:59:09.981696Z"
     },
     "tags": [
      "remove_input"
@@ -92,10 +92,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:33.588261Z",
-     "iopub.status.busy": "2024-06-27T08:57:33.587830Z",
-     "iopub.status.idle": "2024-06-27T08:57:33.590744Z",
-     "shell.execute_reply": "2024-06-27T08:57:33.590243Z"
+     "iopub.execute_input": "2024-06-28T08:59:09.984754Z",
+     "iopub.status.busy": "2024-06-28T08:59:09.984340Z",
+     "iopub.status.idle": "2024-06-28T08:59:09.987176Z",
+     "shell.execute_reply": "2024-06-28T08:59:09.986722Z"
     }
    },
    "outputs": [],
@@ -120,10 +120,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:33.592658Z",
-     "iopub.status.busy": "2024-06-27T08:57:33.592359Z",
-     "iopub.status.idle": "2024-06-27T08:57:34.845915Z",
-     "shell.execute_reply": "2024-06-27T08:57:34.845374Z"
+     "iopub.execute_input": "2024-06-28T08:59:09.989176Z",
+     "iopub.status.busy": "2024-06-28T08:59:09.988827Z",
+     "iopub.status.idle": "2024-06-28T08:59:10.413621Z",
+     "shell.execute_reply": "2024-06-28T08:59:10.413088Z"
     }
    },
    "outputs": [],
@@ -154,10 +154,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:34.848602Z",
-     "iopub.status.busy": "2024-06-27T08:57:34.848198Z",
-     "iopub.status.idle": "2024-06-27T08:57:34.854358Z",
-     "shell.execute_reply": "2024-06-27T08:57:34.853872Z"
+     "iopub.execute_input": "2024-06-28T08:59:10.416134Z",
+     "iopub.status.busy": "2024-06-28T08:59:10.415783Z",
+     "iopub.status.idle": "2024-06-28T08:59:10.421770Z",
+     "shell.execute_reply": "2024-06-28T08:59:10.421210Z"
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     "nbsphinx": "hidden"
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@@ -241,10 +241,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:34.856195Z",
-     "iopub.status.busy": "2024-06-27T08:57:34.856020Z",
-     "iopub.status.idle": "2024-06-27T08:57:34.868966Z",
-     "shell.execute_reply": "2024-06-27T08:57:34.868357Z"
+     "iopub.execute_input": "2024-06-28T08:59:10.423765Z",
+     "iopub.status.busy": "2024-06-28T08:59:10.423443Z",
+     "iopub.status.idle": "2024-06-28T08:59:10.440397Z",
+     "shell.execute_reply": "2024-06-28T08:59:10.439829Z"
     },
     "tags": [
      "remove_input"
@@ -347,10 +347,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:34.871034Z",
-     "iopub.status.busy": "2024-06-27T08:57:34.870866Z",
-     "iopub.status.idle": "2024-06-27T08:57:34.929351Z",
-     "shell.execute_reply": "2024-06-27T08:57:34.928897Z"
+     "iopub.execute_input": "2024-06-28T08:59:10.442472Z",
+     "iopub.status.busy": "2024-06-28T08:59:10.442050Z",
+     "iopub.status.idle": "2024-06-28T08:59:10.500405Z",
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     "tags": [
      "remove_input"
@@ -445,9 +445,9 @@
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        "                            \n",
-       "var gd = document.getElementById('54f81eb1-c71a-4ca3-9450-fd505646b44e');\n",
+       "var gd = document.getElementById('4b0e35ce-a247-4f45-bc82-45045d61c103');\n",
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@@ -496,10 +496,10 @@
    "execution_count": 8,
    "metadata": {
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-     "iopub.execute_input": "2024-06-27T08:57:34.931303Z",
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-     "iopub.status.idle": "2024-06-27T08:57:34.946088Z",
-     "shell.execute_reply": "2024-06-27T08:57:34.945627Z"
+     "iopub.execute_input": "2024-06-28T08:59:10.502238Z",
+     "iopub.status.busy": "2024-06-28T08:59:10.502064Z",
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      "remove_input"
@@ -746,10 +746,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:34.948115Z",
-     "iopub.status.busy": "2024-06-27T08:57:34.947768Z",
-     "iopub.status.idle": "2024-06-27T08:57:34.995763Z",
-     "shell.execute_reply": "2024-06-27T08:57:34.995277Z"
+     "iopub.execute_input": "2024-06-28T08:59:10.519046Z",
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+     "shell.execute_reply": "2024-06-28T08:59:10.566743Z"
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@@ -757,20 +757,20 @@
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        "</style>\n",
-       "<table id=\"T_13005\">\n",
+       "<table id=\"T_63f48\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_13005_level0_col0\" class=\"col_heading level0 col0\" >Positives</th>\n",
-       "      <th id=\"T_13005_level0_col1\" class=\"col_heading level0 col1\" >Negatives</th>\n",
-       "      <th id=\"T_13005_level0_col2\" class=\"col_heading level0 col2\" >Responses %</th>\n",
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-       "      <th id=\"T_13005_level0_col4\" class=\"col_heading level0 col4\" >Negatives %</th>\n",
-       "      <th id=\"T_13005_level0_col5\" class=\"col_heading level0 col5\" >Propensity</th>\n",
+       "      <th id=\"T_63f48_level0_col0\" class=\"col_heading level0 col0\" >Positives</th>\n",
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+       "      <th id=\"T_63f48_level0_col5\" class=\"col_heading level0 col5\" >Propensity</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th class=\"index_name level0\" >Range/Symbol</th>\n",
@@ -784,82 +784,82 @@
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        "  <tbody>\n",
        "    <tr>\n",
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-       "      <td id=\"T_13005_row0_col0\" class=\"data row0 col0\" >13</td>\n",
-       "      <td id=\"T_13005_row0_col1\" class=\"data row0 col1\" >423</td>\n",
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-       "      <td id=\"T_13005_row2_col1\" class=\"data row2 col1\" >250</td>\n",
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-       "      <td id=\"T_13005_row2_col3\" class=\"data row2 col3\" >8.25</td>\n",
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-       "      <td id=\"T_13005_row4_col2\" class=\"data row4 col2\" >5.50</td>\n",
-       "      <td id=\"T_13005_row4_col3\" class=\"data row4 col3\" >3.40</td>\n",
-       "      <td id=\"T_13005_row4_col4\" class=\"data row4 col4\" >5.80</td>\n",
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-       "      <td id=\"T_13005_row5_col2\" class=\"data row5 col2\" >13.57</td>\n",
-       "      <td id=\"T_13005_row5_col3\" class=\"data row5 col3\" >25.73</td>\n",
-       "      <td id=\"T_13005_row5_col4\" class=\"data row5 col4\" >11.82</td>\n",
-       "      <td id=\"T_13005_row5_col5\" class=\"data row5 col5\" >0.2387</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13005_level0_row6\" class=\"row_heading level0 row6\" >[22743.76, 23890.64></th>\n",
-       "      <td id=\"T_13005_row6_col0\" class=\"data row6 col0\" >13</td>\n",
-       "      <td id=\"T_13005_row6_col1\" class=\"data row6 col1\" >77</td>\n",
-       "      <td id=\"T_13005_row6_col2\" class=\"data row6 col2\" >5.50</td>\n",
-       "      <td id=\"T_13005_row6_col3\" class=\"data row6 col3\" >6.31</td>\n",
-       "      <td id=\"T_13005_row6_col4\" class=\"data row6 col4\" >5.38</td>\n",
-       "      <td id=\"T_13005_row6_col5\" class=\"data row6 col5\" >0.1444</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13005_level0_row7\" class=\"row_heading level0 row7\" >>=23890.64</th>\n",
-       "      <td id=\"T_13005_row7_col0\" class=\"data row7 col0\" >28</td>\n",
-       "      <td id=\"T_13005_row7_col1\" class=\"data row7 col1\" >71</td>\n",
-       "      <td id=\"T_13005_row7_col2\" class=\"data row7 col2\" >6.05</td>\n",
-       "      <td id=\"T_13005_row7_col3\" class=\"data row7 col3\" >13.59</td>\n",
-       "      <td id=\"T_13005_row7_col4\" class=\"data row7 col4\" >4.97</td>\n",
-       "      <td id=\"T_13005_row7_col5\" class=\"data row7 col5\" >0.2828</td>\n",
+       "      <th id=\"T_63f48_level0_row0\" class=\"row_heading level0 row0\" ><11684.56</th>\n",
+       "      <td id=\"T_63f48_row0_col0\" class=\"data row0 col0\" >13</td>\n",
+       "      <td id=\"T_63f48_row0_col1\" class=\"data row0 col1\" >423</td>\n",
+       "      <td id=\"T_63f48_row0_col2\" class=\"data row0 col2\" >26.65</td>\n",
+       "      <td id=\"T_63f48_row0_col3\" class=\"data row0 col3\" >6.31</td>\n",
+       "      <td id=\"T_63f48_row0_col4\" class=\"data row0 col4\" >29.58</td>\n",
+       "      <td id=\"T_63f48_row0_col5\" class=\"data row0 col5\" >0.0298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_63f48_level0_row1\" class=\"row_heading level0 row1\" >[11684.56, 13732.56></th>\n",
+       "      <td id=\"T_63f48_row1_col0\" class=\"data row1 col0\" >24</td>\n",
+       "      <td id=\"T_63f48_row1_col1\" class=\"data row1 col1\" >178</td>\n",
+       "      <td id=\"T_63f48_row1_col2\" class=\"data row1 col2\" >12.35</td>\n",
+       "      <td id=\"T_63f48_row1_col3\" class=\"data row1 col3\" >11.65</td>\n",
+       "      <td id=\"T_63f48_row1_col4\" class=\"data row1 col4\" >12.45</td>\n",
+       "      <td id=\"T_63f48_row1_col5\" class=\"data row1 col5\" >0.1188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_63f48_level0_row2\" class=\"row_heading level0 row2\" >[13732.56, 16845.52></th>\n",
+       "      <td id=\"T_63f48_row2_col0\" class=\"data row2 col0\" >17</td>\n",
+       "      <td id=\"T_63f48_row2_col1\" class=\"data row2 col1\" >250</td>\n",
+       "      <td id=\"T_63f48_row2_col2\" class=\"data row2 col2\" >16.32</td>\n",
+       "      <td id=\"T_63f48_row2_col3\" class=\"data row2 col3\" >8.25</td>\n",
+       "      <td id=\"T_63f48_row2_col4\" class=\"data row2 col4\" >17.48</td>\n",
+       "      <td id=\"T_63f48_row2_col5\" class=\"data row2 col5\" >0.0637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_63f48_level0_row3\" class=\"row_heading level0 row3\" >[16845.52, 19139.28></th>\n",
+       "      <td id=\"T_63f48_row3_col0\" class=\"data row3 col0\" >51</td>\n",
+       "      <td id=\"T_63f48_row3_col1\" class=\"data row3 col1\" >179</td>\n",
+       "      <td id=\"T_63f48_row3_col2\" class=\"data row3 col2\" >14.06</td>\n",
+       "      <td id=\"T_63f48_row3_col3\" class=\"data row3 col3\" >24.76</td>\n",
+       "      <td id=\"T_63f48_row3_col4\" class=\"data row3 col4\" >12.52</td>\n",
+       "      <td id=\"T_63f48_row3_col5\" class=\"data row3 col5\" >0.2217</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_63f48_level0_row4\" class=\"row_heading level0 row4\" >[19139.28, 20286.16></th>\n",
+       "      <td id=\"T_63f48_row4_col0\" class=\"data row4 col0\" >7</td>\n",
+       "      <td id=\"T_63f48_row4_col1\" class=\"data row4 col1\" >83</td>\n",
+       "      <td id=\"T_63f48_row4_col2\" class=\"data row4 col2\" >5.50</td>\n",
+       "      <td id=\"T_63f48_row4_col3\" class=\"data row4 col3\" >3.40</td>\n",
+       "      <td id=\"T_63f48_row4_col4\" class=\"data row4 col4\" >5.80</td>\n",
+       "      <td id=\"T_63f48_row4_col5\" class=\"data row4 col5\" >0.0778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_63f48_level0_row5\" class=\"row_heading level0 row5\" >[20286.16, 22743.76></th>\n",
+       "      <td id=\"T_63f48_row5_col0\" class=\"data row5 col0\" >53</td>\n",
+       "      <td id=\"T_63f48_row5_col1\" class=\"data row5 col1\" >169</td>\n",
+       "      <td id=\"T_63f48_row5_col2\" class=\"data row5 col2\" >13.57</td>\n",
+       "      <td id=\"T_63f48_row5_col3\" class=\"data row5 col3\" >25.73</td>\n",
+       "      <td id=\"T_63f48_row5_col4\" class=\"data row5 col4\" >11.82</td>\n",
+       "      <td id=\"T_63f48_row5_col5\" class=\"data row5 col5\" >0.2387</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_63f48_level0_row6\" class=\"row_heading level0 row6\" >[22743.76, 23890.64></th>\n",
+       "      <td id=\"T_63f48_row6_col0\" class=\"data row6 col0\" >13</td>\n",
+       "      <td id=\"T_63f48_row6_col1\" class=\"data row6 col1\" >77</td>\n",
+       "      <td id=\"T_63f48_row6_col2\" class=\"data row6 col2\" >5.50</td>\n",
+       "      <td id=\"T_63f48_row6_col3\" class=\"data row6 col3\" >6.31</td>\n",
+       "      <td id=\"T_63f48_row6_col4\" class=\"data row6 col4\" >5.38</td>\n",
+       "      <td id=\"T_63f48_row6_col5\" class=\"data row6 col5\" >0.1444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_63f48_level0_row7\" class=\"row_heading level0 row7\" >>=23890.64</th>\n",
+       "      <td id=\"T_63f48_row7_col0\" class=\"data row7 col0\" >28</td>\n",
+       "      <td id=\"T_63f48_row7_col1\" class=\"data row7 col1\" >71</td>\n",
+       "      <td id=\"T_63f48_row7_col2\" class=\"data row7 col2\" >6.05</td>\n",
+       "      <td id=\"T_63f48_row7_col3\" class=\"data row7 col3\" >13.59</td>\n",
+       "      <td id=\"T_63f48_row7_col4\" class=\"data row7 col4\" >4.97</td>\n",
+       "      <td id=\"T_63f48_row7_col5\" class=\"data row7 col5\" >0.2828</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7ff198e27b10>"
+       "<pandas.io.formats.style.Styler at 0x7f89ca3f0610>"
       ]
      },
      "execution_count": 9,
@@ -907,10 +907,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:34.997765Z",
-     "iopub.status.busy": "2024-06-27T08:57:34.997393Z",
-     "iopub.status.idle": "2024-06-27T08:57:35.004959Z",
-     "shell.execute_reply": "2024-06-27T08:57:35.004538Z"
+     "iopub.execute_input": "2024-06-28T08:59:10.569161Z",
+     "iopub.status.busy": "2024-06-28T08:59:10.568951Z",
+     "iopub.status.idle": "2024-06-28T08:59:10.576885Z",
+     "shell.execute_reply": "2024-06-28T08:59:10.576448Z"
     }
    },
    "outputs": [
@@ -918,17 +918,17 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_69a9f_row0_col2, #T_69a9f_row1_col2, #T_69a9f_row2_col2, #T_69a9f_row3_col2, #T_69a9f_row4_col2, #T_69a9f_row5_col2, #T_69a9f_row6_col2, #T_69a9f_row7_col2 {\n",
+       "#T_9de26_row0_col2, #T_9de26_row1_col2, #T_9de26_row2_col2, #T_9de26_row3_col2, #T_9de26_row4_col2, #T_9de26_row5_col2, #T_9de26_row6_col2, #T_9de26_row7_col2 {\n",
        "  color: blue;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_69a9f\">\n",
+       "<table id=\"T_9de26\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_69a9f_level0_col0\" class=\"col_heading level0 col0\" >Positives</th>\n",
-       "      <th id=\"T_69a9f_level0_col1\" class=\"col_heading level0 col1\" >Negatives</th>\n",
-       "      <th id=\"T_69a9f_level0_col2\" class=\"col_heading level0 col2\" >Lift</th>\n",
+       "      <th id=\"T_9de26_level0_col0\" class=\"col_heading level0 col0\" >Positives</th>\n",
+       "      <th id=\"T_9de26_level0_col1\" class=\"col_heading level0 col1\" >Negatives</th>\n",
+       "      <th id=\"T_9de26_level0_col2\" class=\"col_heading level0 col2\" >Lift</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th class=\"index_name level0\" >Range/Symbol</th>\n",
@@ -939,58 +939,58 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_69a9f_level0_row0\" class=\"row_heading level0 row0\" ><11684.56</th>\n",
-       "      <td id=\"T_69a9f_row0_col0\" class=\"data row0 col0\" >13</td>\n",
-       "      <td id=\"T_69a9f_row0_col1\" class=\"data row0 col1\" >423</td>\n",
-       "      <td id=\"T_69a9f_row0_col2\" class=\"data row0 col2\" >0.2368</td>\n",
+       "      <th id=\"T_9de26_level0_row0\" class=\"row_heading level0 row0\" ><11684.56</th>\n",
+       "      <td id=\"T_9de26_row0_col0\" class=\"data row0 col0\" >13</td>\n",
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        "    <tr>\n",
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-       "      <td id=\"T_69a9f_row1_col1\" class=\"data row1 col1\" >178</td>\n",
-       "      <td id=\"T_69a9f_row1_col2\" class=\"data row1 col2\" >0.9436</td>\n",
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+       "      <td id=\"T_9de26_row1_col0\" class=\"data row1 col0\" >24</td>\n",
+       "      <td id=\"T_9de26_row1_col1\" class=\"data row1 col1\" >178</td>\n",
+       "      <td id=\"T_9de26_row1_col2\" class=\"data row1 col2\" >0.9436</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_69a9f_level0_row2\" class=\"row_heading level0 row2\" >[13732.56, 16845.52></th>\n",
-       "      <td id=\"T_69a9f_row2_col0\" class=\"data row2 col0\" >17</td>\n",
-       "      <td id=\"T_69a9f_row2_col1\" class=\"data row2 col1\" >250</td>\n",
-       "      <td id=\"T_69a9f_row2_col2\" class=\"data row2 col2\" >0.5057</td>\n",
+       "      <th id=\"T_9de26_level0_row2\" class=\"row_heading level0 row2\" >[13732.56, 16845.52></th>\n",
+       "      <td id=\"T_9de26_row2_col0\" class=\"data row2 col0\" >17</td>\n",
+       "      <td id=\"T_9de26_row2_col1\" class=\"data row2 col1\" >250</td>\n",
+       "      <td id=\"T_9de26_row2_col2\" class=\"data row2 col2\" >0.5057</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_69a9f_level0_row3\" class=\"row_heading level0 row3\" >[16845.52, 19139.28></th>\n",
-       "      <td id=\"T_69a9f_row3_col0\" class=\"data row3 col0\" >51</td>\n",
-       "      <td id=\"T_69a9f_row3_col1\" class=\"data row3 col1\" >179</td>\n",
-       "      <td id=\"T_69a9f_row3_col2\" class=\"data row3 col2\" >1.7610</td>\n",
+       "      <th id=\"T_9de26_level0_row3\" class=\"row_heading level0 row3\" >[16845.52, 19139.28></th>\n",
+       "      <td id=\"T_9de26_row3_col0\" class=\"data row3 col0\" >51</td>\n",
+       "      <td id=\"T_9de26_row3_col1\" class=\"data row3 col1\" >179</td>\n",
+       "      <td id=\"T_9de26_row3_col2\" class=\"data row3 col2\" >1.7610</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_69a9f_level0_row4\" class=\"row_heading level0 row4\" >[19139.28, 20286.16></th>\n",
-       "      <td id=\"T_69a9f_row4_col0\" class=\"data row4 col0\" >7</td>\n",
-       "      <td id=\"T_69a9f_row4_col1\" class=\"data row4 col1\" >83</td>\n",
-       "      <td id=\"T_69a9f_row4_col2\" class=\"data row4 col2\" >0.6177</td>\n",
+       "      <th id=\"T_9de26_level0_row4\" class=\"row_heading level0 row4\" >[19139.28, 20286.16></th>\n",
+       "      <td id=\"T_9de26_row4_col0\" class=\"data row4 col0\" >7</td>\n",
+       "      <td id=\"T_9de26_row4_col1\" class=\"data row4 col1\" >83</td>\n",
+       "      <td id=\"T_9de26_row4_col2\" class=\"data row4 col2\" >0.6177</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_69a9f_level0_row5\" class=\"row_heading level0 row5\" >[20286.16, 22743.76></th>\n",
-       "      <td id=\"T_69a9f_row5_col0\" class=\"data row5 col0\" >53</td>\n",
-       "      <td id=\"T_69a9f_row5_col1\" class=\"data row5 col1\" >169</td>\n",
-       "      <td id=\"T_69a9f_row5_col2\" class=\"data row5 col2\" >1.8960</td>\n",
+       "      <th id=\"T_9de26_level0_row5\" class=\"row_heading level0 row5\" >[20286.16, 22743.76></th>\n",
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+       "      <td id=\"T_9de26_row5_col1\" class=\"data row5 col1\" >169</td>\n",
+       "      <td id=\"T_9de26_row5_col2\" class=\"data row5 col2\" >1.8960</td>\n",
        "    </tr>\n",
        "    <tr>\n",
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-       "      <td id=\"T_69a9f_row6_col1\" class=\"data row6 col1\" >77</td>\n",
-       "      <td id=\"T_69a9f_row6_col2\" class=\"data row6 col2\" >1.1471</td>\n",
+       "      <th id=\"T_9de26_level0_row6\" class=\"row_heading level0 row6\" >[22743.76, 23890.64></th>\n",
+       "      <td id=\"T_9de26_row6_col0\" class=\"data row6 col0\" >13</td>\n",
+       "      <td id=\"T_9de26_row6_col1\" class=\"data row6 col1\" >77</td>\n",
+       "      <td id=\"T_9de26_row6_col2\" class=\"data row6 col2\" >1.1471</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_69a9f_level0_row7\" class=\"row_heading level0 row7\" >>=23890.64</th>\n",
-       "      <td id=\"T_69a9f_row7_col0\" class=\"data row7 col0\" >28</td>\n",
-       "      <td id=\"T_69a9f_row7_col1\" class=\"data row7 col1\" >71</td>\n",
-       "      <td id=\"T_69a9f_row7_col2\" class=\"data row7 col2\" >2.2462</td>\n",
+       "      <th id=\"T_9de26_level0_row7\" class=\"row_heading level0 row7\" >>=23890.64</th>\n",
+       "      <td id=\"T_9de26_row7_col0\" class=\"data row7 col0\" >28</td>\n",
+       "      <td id=\"T_9de26_row7_col1\" class=\"data row7 col1\" >71</td>\n",
+       "      <td id=\"T_9de26_row7_col2\" class=\"data row7 col2\" >2.2462</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7ff13cfcd590>"
+       "<pandas.io.formats.style.Styler at 0x7f89b97e21d0>"
       ]
      },
      "execution_count": 10,
@@ -1040,10 +1040,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:35.006787Z",
-     "iopub.status.busy": "2024-06-27T08:57:35.006582Z",
-     "iopub.status.idle": "2024-06-27T08:57:35.017471Z",
-     "shell.execute_reply": "2024-06-27T08:57:35.016935Z"
+     "iopub.execute_input": "2024-06-28T08:59:10.578882Z",
+     "iopub.status.busy": "2024-06-28T08:59:10.578559Z",
+     "iopub.status.idle": "2024-06-28T08:59:10.589366Z",
+     "shell.execute_reply": "2024-06-28T08:59:10.588923Z"
     }
    },
    "outputs": [
@@ -1051,19 +1051,19 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_e50b4_row0_col4, #T_e50b4_row1_col4, #T_e50b4_row2_col4, #T_e50b4_row3_col4, #T_e50b4_row4_col4, #T_e50b4_row5_col4, #T_e50b4_row6_col4, #T_e50b4_row7_col4 {\n",
+       "#T_2a0af_row0_col4, #T_2a0af_row1_col4, #T_2a0af_row2_col4, #T_2a0af_row3_col4, #T_2a0af_row4_col4, #T_2a0af_row5_col4, #T_2a0af_row6_col4, #T_2a0af_row7_col4 {\n",
        "  color: blue;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_e50b4\">\n",
+       "<table id=\"T_2a0af\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_e50b4_level0_col0\" class=\"col_heading level0 col0\" >Positives</th>\n",
-       "      <th id=\"T_e50b4_level0_col1\" class=\"col_heading level0 col1\" >Negatives</th>\n",
-       "      <th id=\"T_e50b4_level0_col2\" class=\"col_heading level0 col2\" >Positives %</th>\n",
-       "      <th id=\"T_e50b4_level0_col3\" class=\"col_heading level0 col3\" >Negatives %</th>\n",
-       "      <th id=\"T_e50b4_level0_col4\" class=\"col_heading level0 col4\" >ZRatio</th>\n",
+       "      <th id=\"T_2a0af_level0_col0\" class=\"col_heading level0 col0\" >Positives</th>\n",
+       "      <th id=\"T_2a0af_level0_col1\" class=\"col_heading level0 col1\" >Negatives</th>\n",
+       "      <th id=\"T_2a0af_level0_col2\" class=\"col_heading level0 col2\" >Positives %</th>\n",
+       "      <th id=\"T_2a0af_level0_col3\" class=\"col_heading level0 col3\" >Negatives %</th>\n",
+       "      <th id=\"T_2a0af_level0_col4\" class=\"col_heading level0 col4\" >ZRatio</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th class=\"index_name level0\" >Range/Symbol</th>\n",
@@ -1076,74 +1076,74 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_e50b4_level0_row0\" class=\"row_heading level0 row0\" ><11684.56</th>\n",
-       "      <td id=\"T_e50b4_row0_col0\" class=\"data row0 col0\" >13</td>\n",
-       "      <td id=\"T_e50b4_row0_col1\" class=\"data row0 col1\" >423</td>\n",
-       "      <td id=\"T_e50b4_row0_col2\" class=\"data row0 col2\" >6.31</td>\n",
-       "      <td id=\"T_e50b4_row0_col3\" class=\"data row0 col3\" >29.58</td>\n",
-       "      <td id=\"T_e50b4_row0_col4\" class=\"data row0 col4\" >-11.1869</td>\n",
+       "      <th id=\"T_2a0af_level0_row0\" class=\"row_heading level0 row0\" ><11684.56</th>\n",
+       "      <td id=\"T_2a0af_row0_col0\" class=\"data row0 col0\" >13</td>\n",
+       "      <td id=\"T_2a0af_row0_col1\" class=\"data row0 col1\" >423</td>\n",
+       "      <td id=\"T_2a0af_row0_col2\" class=\"data row0 col2\" >6.31</td>\n",
+       "      <td id=\"T_2a0af_row0_col3\" class=\"data row0 col3\" >29.58</td>\n",
+       "      <td id=\"T_2a0af_row0_col4\" class=\"data row0 col4\" >-11.1869</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e50b4_level0_row1\" class=\"row_heading level0 row1\" >[11684.56, 13732.56></th>\n",
-       "      <td id=\"T_e50b4_row1_col0\" class=\"data row1 col0\" >24</td>\n",
-       "      <td id=\"T_e50b4_row1_col1\" class=\"data row1 col1\" >178</td>\n",
-       "      <td id=\"T_e50b4_row1_col2\" class=\"data row1 col2\" >11.65</td>\n",
-       "      <td id=\"T_e50b4_row1_col3\" class=\"data row1 col3\" >12.45</td>\n",
-       "      <td id=\"T_e50b4_row1_col4\" class=\"data row1 col4\" >-0.3321</td>\n",
+       "      <th id=\"T_2a0af_level0_row1\" class=\"row_heading level0 row1\" >[11684.56, 13732.56></th>\n",
+       "      <td id=\"T_2a0af_row1_col0\" class=\"data row1 col0\" >24</td>\n",
+       "      <td id=\"T_2a0af_row1_col1\" class=\"data row1 col1\" >178</td>\n",
+       "      <td id=\"T_2a0af_row1_col2\" class=\"data row1 col2\" >11.65</td>\n",
+       "      <td id=\"T_2a0af_row1_col3\" class=\"data row1 col3\" >12.45</td>\n",
+       "      <td id=\"T_2a0af_row1_col4\" class=\"data row1 col4\" >-0.3321</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e50b4_level0_row2\" class=\"row_heading level0 row2\" >[13732.56, 16845.52></th>\n",
-       "      <td id=\"T_e50b4_row2_col0\" class=\"data row2 col0\" >17</td>\n",
-       "      <td id=\"T_e50b4_row2_col1\" class=\"data row2 col1\" >250</td>\n",
-       "      <td id=\"T_e50b4_row2_col2\" class=\"data row2 col2\" >8.25</td>\n",
-       "      <td id=\"T_e50b4_row2_col3\" class=\"data row2 col3\" >17.48</td>\n",
-       "      <td id=\"T_e50b4_row2_col4\" class=\"data row2 col4\" >-4.2647</td>\n",
+       "      <th id=\"T_2a0af_level0_row2\" class=\"row_heading level0 row2\" >[13732.56, 16845.52></th>\n",
+       "      <td id=\"T_2a0af_row2_col0\" class=\"data row2 col0\" >17</td>\n",
+       "      <td id=\"T_2a0af_row2_col1\" class=\"data row2 col1\" >250</td>\n",
+       "      <td id=\"T_2a0af_row2_col2\" class=\"data row2 col2\" >8.25</td>\n",
+       "      <td id=\"T_2a0af_row2_col3\" class=\"data row2 col3\" >17.48</td>\n",
+       "      <td id=\"T_2a0af_row2_col4\" class=\"data row2 col4\" >-4.2647</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e50b4_level0_row3\" class=\"row_heading level0 row3\" >[16845.52, 19139.28></th>\n",
-       "      <td id=\"T_e50b4_row3_col0\" class=\"data row3 col0\" >51</td>\n",
-       "      <td id=\"T_e50b4_row3_col1\" class=\"data row3 col1\" >179</td>\n",
-       "      <td id=\"T_e50b4_row3_col2\" class=\"data row3 col2\" >24.76</td>\n",
-       "      <td id=\"T_e50b4_row3_col3\" class=\"data row3 col3\" >12.52</td>\n",
-       "      <td id=\"T_e50b4_row3_col4\" class=\"data row3 col4\" >3.9082</td>\n",
+       "      <th id=\"T_2a0af_level0_row3\" class=\"row_heading level0 row3\" >[16845.52, 19139.28></th>\n",
+       "      <td id=\"T_2a0af_row3_col0\" class=\"data row3 col0\" >51</td>\n",
+       "      <td id=\"T_2a0af_row3_col1\" class=\"data row3 col1\" >179</td>\n",
+       "      <td id=\"T_2a0af_row3_col2\" class=\"data row3 col2\" >24.76</td>\n",
+       "      <td id=\"T_2a0af_row3_col3\" class=\"data row3 col3\" >12.52</td>\n",
+       "      <td id=\"T_2a0af_row3_col4\" class=\"data row3 col4\" >3.9082</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e50b4_level0_row4\" class=\"row_heading level0 row4\" >[19139.28, 20286.16></th>\n",
-       "      <td id=\"T_e50b4_row4_col0\" class=\"data row4 col0\" >7</td>\n",
-       "      <td id=\"T_e50b4_row4_col1\" class=\"data row4 col1\" >83</td>\n",
-       "      <td id=\"T_e50b4_row4_col2\" class=\"data row4 col2\" >3.40</td>\n",
-       "      <td id=\"T_e50b4_row4_col3\" class=\"data row4 col3\" >5.80</td>\n",
-       "      <td id=\"T_e50b4_row4_col4\" class=\"data row4 col4\" >-1.7118</td>\n",
+       "      <th id=\"T_2a0af_level0_row4\" class=\"row_heading level0 row4\" >[19139.28, 20286.16></th>\n",
+       "      <td id=\"T_2a0af_row4_col0\" class=\"data row4 col0\" >7</td>\n",
+       "      <td id=\"T_2a0af_row4_col1\" class=\"data row4 col1\" >83</td>\n",
+       "      <td id=\"T_2a0af_row4_col2\" class=\"data row4 col2\" >3.40</td>\n",
+       "      <td id=\"T_2a0af_row4_col3\" class=\"data row4 col3\" >5.80</td>\n",
+       "      <td id=\"T_2a0af_row4_col4\" class=\"data row4 col4\" >-1.7118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e50b4_level0_row5\" class=\"row_heading level0 row5\" >[20286.16, 22743.76></th>\n",
-       "      <td id=\"T_e50b4_row5_col0\" class=\"data row5 col0\" >53</td>\n",
-       "      <td id=\"T_e50b4_row5_col1\" class=\"data row5 col1\" >169</td>\n",
-       "      <td id=\"T_e50b4_row5_col2\" class=\"data row5 col2\" >25.73</td>\n",
-       "      <td id=\"T_e50b4_row5_col3\" class=\"data row5 col3\" >11.82</td>\n",
-       "      <td id=\"T_e50b4_row5_col4\" class=\"data row5 col4\" >4.3976</td>\n",
+       "      <th id=\"T_2a0af_level0_row5\" class=\"row_heading level0 row5\" >[20286.16, 22743.76></th>\n",
+       "      <td id=\"T_2a0af_row5_col0\" class=\"data row5 col0\" >53</td>\n",
+       "      <td id=\"T_2a0af_row5_col1\" class=\"data row5 col1\" >169</td>\n",
+       "      <td id=\"T_2a0af_row5_col2\" class=\"data row5 col2\" >25.73</td>\n",
+       "      <td id=\"T_2a0af_row5_col3\" class=\"data row5 col3\" >11.82</td>\n",
+       "      <td id=\"T_2a0af_row5_col4\" class=\"data row5 col4\" >4.3976</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e50b4_level0_row6\" class=\"row_heading level0 row6\" >[22743.76, 23890.64></th>\n",
-       "      <td id=\"T_e50b4_row6_col0\" class=\"data row6 col0\" >13</td>\n",
-       "      <td id=\"T_e50b4_row6_col1\" class=\"data row6 col1\" >77</td>\n",
-       "      <td id=\"T_e50b4_row6_col2\" class=\"data row6 col2\" >6.31</td>\n",
-       "      <td id=\"T_e50b4_row6_col3\" class=\"data row6 col3\" >5.38</td>\n",
-       "      <td id=\"T_e50b4_row6_col4\" class=\"data row6 col4\" >0.5156</td>\n",
+       "      <th id=\"T_2a0af_level0_row6\" class=\"row_heading level0 row6\" >[22743.76, 23890.64></th>\n",
+       "      <td id=\"T_2a0af_row6_col0\" class=\"data row6 col0\" >13</td>\n",
+       "      <td id=\"T_2a0af_row6_col1\" class=\"data row6 col1\" >77</td>\n",
+       "      <td id=\"T_2a0af_row6_col2\" class=\"data row6 col2\" >6.31</td>\n",
+       "      <td id=\"T_2a0af_row6_col3\" class=\"data row6 col3\" >5.38</td>\n",
+       "      <td id=\"T_2a0af_row6_col4\" class=\"data row6 col4\" >0.5156</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e50b4_level0_row7\" class=\"row_heading level0 row7\" >>=23890.64</th>\n",
-       "      <td id=\"T_e50b4_row7_col0\" class=\"data row7 col0\" >28</td>\n",
-       "      <td id=\"T_e50b4_row7_col1\" class=\"data row7 col1\" >71</td>\n",
-       "      <td id=\"T_e50b4_row7_col2\" class=\"data row7 col2\" >13.59</td>\n",
-       "      <td id=\"T_e50b4_row7_col3\" class=\"data row7 col3\" >4.97</td>\n",
-       "      <td id=\"T_e50b4_row7_col4\" class=\"data row7 col4\" >3.5129</td>\n",
+       "      <th id=\"T_2a0af_level0_row7\" class=\"row_heading level0 row7\" >>=23890.64</th>\n",
+       "      <td id=\"T_2a0af_row7_col0\" class=\"data row7 col0\" >28</td>\n",
+       "      <td id=\"T_2a0af_row7_col1\" class=\"data row7 col1\" >71</td>\n",
+       "      <td id=\"T_2a0af_row7_col2\" class=\"data row7 col2\" >13.59</td>\n",
+       "      <td id=\"T_2a0af_row7_col3\" class=\"data row7 col3\" >4.97</td>\n",
+       "      <td id=\"T_2a0af_row7_col4\" class=\"data row7 col4\" >3.5129</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7ff13cf94c50>"
+       "<pandas.io.formats.style.Styler at 0x7f89b86dc0d0>"
       ]
      },
      "execution_count": 11,
@@ -1208,19 +1208,19 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:35.019508Z",
-     "iopub.status.busy": "2024-06-27T08:57:35.019242Z",
-     "iopub.status.idle": "2024-06-27T08:57:35.047604Z",
-     "shell.execute_reply": "2024-06-27T08:57:35.047160Z"
+     "iopub.execute_input": "2024-06-28T08:59:10.591399Z",
+     "iopub.status.busy": "2024-06-28T08:59:10.591002Z",
+     "iopub.status.idle": "2024-06-28T08:59:10.619550Z",
+     "shell.execute_reply": "2024-06-28T08:59:10.619067Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<div>                            <div id=\"2f661812-04d3-4ae7-8d40-110161cfb4ec\" class=\"plotly-graph-div\" style=\"height:700px; width:700px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"2f661812-04d3-4ae7-8d40-110161cfb4ec\")) {                    Plotly.newPlot(                        \"2f661812-04d3-4ae7-8d40-110161cfb4ec\",                        [{\"hovertemplate\":\"Specificity=%{x}\\u003cbr\\u003eSensitivity=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"\",\"line\":{\"color\":\"#1F77B4\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[1.0,0.9368932247161865,0.8543689250946045,0.8203883171081543,0.7038835287094116,0.6407766938209534,0.39320385456085205,0.13592231273651123,0.0],\"xaxis\":\"x\",\"y\":[0.0,0.2958042025566101,0.47062936425209045,0.5286713242530823,0.6531468629837036,0.7069929838180542,0.8321678042411804,0.9503496289253235,1.0],\"yaxis\":\"y\",\"type\":\"scatter\"}],                        {\"template\":{\"data\":{\"scatter\":[{\"type\":\"scatter\"}]}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Specificity\"},\"range\":[1,0]},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Sensitivity\"}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"AUC = 0.722\"},\"height\":700,\"width\":700,\"shapes\":[{\"line\":{\"dash\":\"dash\"},\"type\":\"line\",\"x0\":1,\"x1\":0,\"y0\":0,\"y1\":1}]},                        {\"responsive\": true}                    ).then(function(){\n",
+       "<div>                            <div id=\"70112958-3d75-4db1-b602-02d61e635918\" class=\"plotly-graph-div\" style=\"height:700px; width:700px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"70112958-3d75-4db1-b602-02d61e635918\")) {                    Plotly.newPlot(                        \"70112958-3d75-4db1-b602-02d61e635918\",                        [{\"hovertemplate\":\"Specificity=%{x}\\u003cbr\\u003eSensitivity=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"\",\"line\":{\"color\":\"#1F77B4\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[1.0,0.9368932247161865,0.8543689250946045,0.8203883171081543,0.7038835287094116,0.6407766938209534,0.39320385456085205,0.13592231273651123,0.0],\"xaxis\":\"x\",\"y\":[0.0,0.2958042025566101,0.47062936425209045,0.5286713242530823,0.6531468629837036,0.7069929838180542,0.8321678042411804,0.9503496289253235,1.0],\"yaxis\":\"y\",\"type\":\"scatter\"}],                        {\"template\":{\"data\":{\"scatter\":[{\"type\":\"scatter\"}]}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Specificity\"},\"range\":[1,0]},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Sensitivity\"}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"AUC = 0.722\"},\"height\":700,\"width\":700,\"shapes\":[{\"line\":{\"dash\":\"dash\"},\"type\":\"line\",\"x0\":1,\"x1\":0,\"y0\":0,\"y1\":1}]},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('2f661812-04d3-4ae7-8d40-110161cfb4ec');\n",
+       "var gd = document.getElementById('70112958-3d75-4db1-b602-02d61e635918');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
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@@ -1332,10 +1332,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:35.049658Z",
-     "iopub.status.busy": "2024-06-27T08:57:35.049338Z",
-     "iopub.status.idle": "2024-06-27T08:57:35.058639Z",
-     "shell.execute_reply": "2024-06-27T08:57:35.058109Z"
+     "iopub.execute_input": "2024-06-28T08:59:10.621629Z",
+     "iopub.status.busy": "2024-06-28T08:59:10.621297Z",
+     "iopub.status.idle": "2024-06-28T08:59:10.630204Z",
+     "shell.execute_reply": "2024-06-28T08:59:10.629766Z"
     }
    },
    "outputs": [
@@ -1343,20 +1343,20 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_2980d_row0_col4, #T_2980d_row0_col5, #T_2980d_row1_col4, #T_2980d_row1_col5, #T_2980d_row2_col4, #T_2980d_row2_col5, #T_2980d_row3_col4, #T_2980d_row3_col5, #T_2980d_row4_col4, #T_2980d_row4_col5, #T_2980d_row5_col4, #T_2980d_row5_col5, #T_2980d_row6_col4, #T_2980d_row6_col5, #T_2980d_row7_col4, #T_2980d_row7_col5 {\n",
+       "#T_f77c7_row0_col4, #T_f77c7_row0_col5, #T_f77c7_row1_col4, #T_f77c7_row1_col5, #T_f77c7_row2_col4, #T_f77c7_row2_col5, #T_f77c7_row3_col4, #T_f77c7_row3_col5, #T_f77c7_row4_col4, #T_f77c7_row4_col5, #T_f77c7_row5_col4, #T_f77c7_row5_col5, #T_f77c7_row6_col4, #T_f77c7_row6_col5, #T_f77c7_row7_col4, #T_f77c7_row7_col5 {\n",
        "  color: blue;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_2980d\">\n",
+       "<table id=\"T_f77c7\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_2980d_level0_col0\" class=\"col_heading level0 col0\" >Positives</th>\n",
-       "      <th id=\"T_2980d_level0_col1\" class=\"col_heading level0 col1\" >Negatives</th>\n",
-       "      <th id=\"T_2980d_level0_col2\" class=\"col_heading level0 col2\" >Positives %</th>\n",
-       "      <th id=\"T_2980d_level0_col3\" class=\"col_heading level0 col3\" >Negatives %</th>\n",
-       "      <th id=\"T_2980d_level0_col4\" class=\"col_heading level0 col4\" >LogOdds</th>\n",
-       "      <th id=\"T_2980d_level0_col5\" class=\"col_heading level0 col5\" >ModifiedLogOdds</th>\n",
+       "      <th id=\"T_f77c7_level0_col0\" class=\"col_heading level0 col0\" >Positives</th>\n",
+       "      <th id=\"T_f77c7_level0_col1\" class=\"col_heading level0 col1\" >Negatives</th>\n",
+       "      <th id=\"T_f77c7_level0_col2\" class=\"col_heading level0 col2\" >Positives %</th>\n",
+       "      <th id=\"T_f77c7_level0_col3\" class=\"col_heading level0 col3\" >Negatives %</th>\n",
+       "      <th id=\"T_f77c7_level0_col4\" class=\"col_heading level0 col4\" >LogOdds</th>\n",
+       "      <th id=\"T_f77c7_level0_col5\" class=\"col_heading level0 col5\" >ModifiedLogOdds</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th class=\"index_name level0\" >Range/Symbol</th>\n",
@@ -1370,82 +1370,82 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_2980d_level0_row0\" class=\"row_heading level0 row0\" ><11684.56</th>\n",
-       "      <td id=\"T_2980d_row0_col0\" class=\"data row0 col0\" >13</td>\n",
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-       "      <td id=\"T_2980d_row0_col2\" class=\"data row0 col2\" >6.31</td>\n",
-       "      <td id=\"T_2980d_row0_col3\" class=\"data row0 col3\" >29.580000</td>\n",
-       "      <td id=\"T_2980d_row0_col4\" class=\"data row0 col4\" >-1.544963</td>\n",
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-       "      <th id=\"T_2980d_level0_row1\" class=\"row_heading level0 row1\" >[11684.56, 13732.56></th>\n",
-       "      <td id=\"T_2980d_row1_col0\" class=\"data row1 col0\" >24</td>\n",
-       "      <td id=\"T_2980d_row1_col1\" class=\"data row1 col1\" >178</td>\n",
-       "      <td id=\"T_2980d_row1_col2\" class=\"data row1 col2\" >11.65</td>\n",
-       "      <td id=\"T_2980d_row1_col3\" class=\"data row1 col3\" >12.450000</td>\n",
-       "      <td id=\"T_2980d_row1_col4\" class=\"data row1 col4\" >-0.066414</td>\n",
-       "      <td id=\"T_2980d_row1_col5\" class=\"data row1 col5\" >-0.0658</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2980d_level0_row2\" class=\"row_heading level0 row2\" >[13732.56, 16845.52></th>\n",
-       "      <td id=\"T_2980d_row2_col0\" class=\"data row2 col0\" >17</td>\n",
-       "      <td id=\"T_2980d_row2_col1\" class=\"data row2 col1\" >250</td>\n",
-       "      <td id=\"T_2980d_row2_col2\" class=\"data row2 col2\" >8.25</td>\n",
-       "      <td id=\"T_2980d_row2_col3\" class=\"data row2 col3\" >17.480000</td>\n",
-       "      <td id=\"T_2980d_row2_col4\" class=\"data row2 col4\" >-0.750844</td>\n",
-       "      <td id=\"T_2980d_row2_col5\" class=\"data row2 col5\" >-0.7480</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2980d_level0_row3\" class=\"row_heading level0 row3\" >[16845.52, 19139.28></th>\n",
-       "      <td id=\"T_2980d_row3_col0\" class=\"data row3 col0\" >51</td>\n",
-       "      <td id=\"T_2980d_row3_col1\" class=\"data row3 col1\" >179</td>\n",
-       "      <td id=\"T_2980d_row3_col2\" class=\"data row3 col2\" >24.76</td>\n",
-       "      <td id=\"T_2980d_row3_col3\" class=\"data row3 col3\" >12.520000</td>\n",
-       "      <td id=\"T_2980d_row3_col4\" class=\"data row3 col4\" >0.681902</td>\n",
-       "      <td id=\"T_2980d_row3_col5\" class=\"data row3 col5\" >0.6796</td>\n",
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-       "      <th id=\"T_2980d_level0_row4\" class=\"row_heading level0 row4\" >[19139.28, 20286.16></th>\n",
-       "      <td id=\"T_2980d_row4_col0\" class=\"data row4 col0\" >7</td>\n",
-       "      <td id=\"T_2980d_row4_col1\" class=\"data row4 col1\" >83</td>\n",
-       "      <td id=\"T_2980d_row4_col2\" class=\"data row4 col2\" >3.40</td>\n",
-       "      <td id=\"T_2980d_row4_col3\" class=\"data row4 col3\" >5.800000</td>\n",
-       "      <td id=\"T_2980d_row4_col4\" class=\"data row4 col4\" >-0.534083</td>\n",
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-       "      <th id=\"T_2980d_level0_row5\" class=\"row_heading level0 row5\" >[20286.16, 22743.76></th>\n",
-       "      <td id=\"T_2980d_row5_col0\" class=\"data row5 col0\" >53</td>\n",
-       "      <td id=\"T_2980d_row5_col1\" class=\"data row5 col1\" >169</td>\n",
-       "      <td id=\"T_2980d_row5_col2\" class=\"data row5 col2\" >25.73</td>\n",
-       "      <td id=\"T_2980d_row5_col3\" class=\"data row5 col3\" >11.820000</td>\n",
-       "      <td id=\"T_2980d_row5_col4\" class=\"data row5 col4\" >0.777865</td>\n",
-       "      <td id=\"T_2980d_row5_col5\" class=\"data row5 col5\" >0.7754</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2980d_level0_row6\" class=\"row_heading level0 row6\" >[22743.76, 23890.64></th>\n",
-       "      <td id=\"T_2980d_row6_col0\" class=\"data row6 col0\" >13</td>\n",
-       "      <td id=\"T_2980d_row6_col1\" class=\"data row6 col1\" >77</td>\n",
-       "      <td id=\"T_2980d_row6_col2\" class=\"data row6 col2\" >6.31</td>\n",
-       "      <td id=\"T_2980d_row6_col3\" class=\"data row6 col3\" >5.380000</td>\n",
-       "      <td id=\"T_2980d_row6_col4\" class=\"data row6 col4\" >0.159447</td>\n",
-       "      <td id=\"T_2980d_row6_col5\" class=\"data row6 col5\" >0.1625</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2980d_level0_row7\" class=\"row_heading level0 row7\" >>=23890.64</th>\n",
-       "      <td id=\"T_2980d_row7_col0\" class=\"data row7 col0\" >28</td>\n",
-       "      <td id=\"T_2980d_row7_col1\" class=\"data row7 col1\" >71</td>\n",
-       "      <td id=\"T_2980d_row7_col2\" class=\"data row7 col2\" >13.59</td>\n",
-       "      <td id=\"T_2980d_row7_col3\" class=\"data row7 col3\" >4.970000</td>\n",
-       "      <td id=\"T_2980d_row7_col4\" class=\"data row7 col4\" >1.005915</td>\n",
-       "      <td id=\"T_2980d_row7_col5\" class=\"data row7 col5\" >1.0056</td>\n",
+       "      <th id=\"T_f77c7_level0_row0\" class=\"row_heading level0 row0\" ><11684.56</th>\n",
+       "      <td id=\"T_f77c7_row0_col0\" class=\"data row0 col0\" >13</td>\n",
+       "      <td id=\"T_f77c7_row0_col1\" class=\"data row0 col1\" >423</td>\n",
+       "      <td id=\"T_f77c7_row0_col2\" class=\"data row0 col2\" >6.31</td>\n",
+       "      <td id=\"T_f77c7_row0_col3\" class=\"data row0 col3\" >29.580000</td>\n",
+       "      <td id=\"T_f77c7_row0_col4\" class=\"data row0 col4\" >-1.544963</td>\n",
+       "      <td id=\"T_f77c7_row0_col5\" class=\"data row0 col5\" >-1.5397</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f77c7_level0_row1\" class=\"row_heading level0 row1\" >[11684.56, 13732.56></th>\n",
+       "      <td id=\"T_f77c7_row1_col0\" class=\"data row1 col0\" >24</td>\n",
+       "      <td id=\"T_f77c7_row1_col1\" class=\"data row1 col1\" >178</td>\n",
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+       "      <td id=\"T_f77c7_row1_col3\" class=\"data row1 col3\" >12.450000</td>\n",
+       "      <td id=\"T_f77c7_row1_col4\" class=\"data row1 col4\" >-0.066414</td>\n",
+       "      <td id=\"T_f77c7_row1_col5\" class=\"data row1 col5\" >-0.0658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f77c7_level0_row2\" class=\"row_heading level0 row2\" >[13732.56, 16845.52></th>\n",
+       "      <td id=\"T_f77c7_row2_col0\" class=\"data row2 col0\" >17</td>\n",
+       "      <td id=\"T_f77c7_row2_col1\" class=\"data row2 col1\" >250</td>\n",
+       "      <td id=\"T_f77c7_row2_col2\" class=\"data row2 col2\" >8.25</td>\n",
+       "      <td id=\"T_f77c7_row2_col3\" class=\"data row2 col3\" >17.480000</td>\n",
+       "      <td id=\"T_f77c7_row2_col4\" class=\"data row2 col4\" >-0.750844</td>\n",
+       "      <td id=\"T_f77c7_row2_col5\" class=\"data row2 col5\" >-0.7480</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f77c7_level0_row3\" class=\"row_heading level0 row3\" >[16845.52, 19139.28></th>\n",
+       "      <td id=\"T_f77c7_row3_col0\" class=\"data row3 col0\" >51</td>\n",
+       "      <td id=\"T_f77c7_row3_col1\" class=\"data row3 col1\" >179</td>\n",
+       "      <td id=\"T_f77c7_row3_col2\" class=\"data row3 col2\" >24.76</td>\n",
+       "      <td id=\"T_f77c7_row3_col3\" class=\"data row3 col3\" >12.520000</td>\n",
+       "      <td id=\"T_f77c7_row3_col4\" class=\"data row3 col4\" >0.681902</td>\n",
+       "      <td id=\"T_f77c7_row3_col5\" class=\"data row3 col5\" >0.6796</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f77c7_level0_row4\" class=\"row_heading level0 row4\" >[19139.28, 20286.16></th>\n",
+       "      <td id=\"T_f77c7_row4_col0\" class=\"data row4 col0\" >7</td>\n",
+       "      <td id=\"T_f77c7_row4_col1\" class=\"data row4 col1\" >83</td>\n",
+       "      <td id=\"T_f77c7_row4_col2\" class=\"data row4 col2\" >3.40</td>\n",
+       "      <td id=\"T_f77c7_row4_col3\" class=\"data row4 col3\" >5.800000</td>\n",
+       "      <td id=\"T_f77c7_row4_col4\" class=\"data row4 col4\" >-0.534083</td>\n",
+       "      <td id=\"T_f77c7_row4_col5\" class=\"data row4 col5\" >-0.5233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f77c7_level0_row5\" class=\"row_heading level0 row5\" >[20286.16, 22743.76></th>\n",
+       "      <td id=\"T_f77c7_row5_col0\" class=\"data row5 col0\" >53</td>\n",
+       "      <td id=\"T_f77c7_row5_col1\" class=\"data row5 col1\" >169</td>\n",
+       "      <td id=\"T_f77c7_row5_col2\" class=\"data row5 col2\" >25.73</td>\n",
+       "      <td id=\"T_f77c7_row5_col3\" class=\"data row5 col3\" >11.820000</td>\n",
+       "      <td id=\"T_f77c7_row5_col4\" class=\"data row5 col4\" >0.777865</td>\n",
+       "      <td id=\"T_f77c7_row5_col5\" class=\"data row5 col5\" >0.7754</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f77c7_level0_row6\" class=\"row_heading level0 row6\" >[22743.76, 23890.64></th>\n",
+       "      <td id=\"T_f77c7_row6_col0\" class=\"data row6 col0\" >13</td>\n",
+       "      <td id=\"T_f77c7_row6_col1\" class=\"data row6 col1\" >77</td>\n",
+       "      <td id=\"T_f77c7_row6_col2\" class=\"data row6 col2\" >6.31</td>\n",
+       "      <td id=\"T_f77c7_row6_col3\" class=\"data row6 col3\" >5.380000</td>\n",
+       "      <td id=\"T_f77c7_row6_col4\" class=\"data row6 col4\" >0.159447</td>\n",
+       "      <td id=\"T_f77c7_row6_col5\" class=\"data row6 col5\" >0.1625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f77c7_level0_row7\" class=\"row_heading level0 row7\" >>=23890.64</th>\n",
+       "      <td id=\"T_f77c7_row7_col0\" class=\"data row7 col0\" >28</td>\n",
+       "      <td id=\"T_f77c7_row7_col1\" class=\"data row7 col1\" >71</td>\n",
+       "      <td id=\"T_f77c7_row7_col2\" class=\"data row7 col2\" >13.59</td>\n",
+       "      <td id=\"T_f77c7_row7_col3\" class=\"data row7 col3\" >4.970000</td>\n",
+       "      <td id=\"T_f77c7_row7_col4\" class=\"data row7 col4\" >1.005915</td>\n",
+       "      <td id=\"T_f77c7_row7_col5\" class=\"data row7 col5\" >1.0056</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7ff128d3f750>"
+       "<pandas.io.formats.style.Styler at 0x7f8986e078d0>"
       ]
      },
      "execution_count": 13,
@@ -1510,10 +1510,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:35.060697Z",
-     "iopub.status.busy": "2024-06-27T08:57:35.060391Z",
-     "iopub.status.idle": "2024-06-27T08:57:35.085920Z",
-     "shell.execute_reply": "2024-06-27T08:57:35.085389Z"
+     "iopub.execute_input": "2024-06-28T08:59:10.632370Z",
+     "iopub.status.busy": "2024-06-28T08:59:10.632054Z",
+     "iopub.status.idle": "2024-06-28T08:59:10.657288Z",
+     "shell.execute_reply": "2024-06-28T08:59:10.656763Z"
     },
     "tags": [
      "remove_input"
@@ -1524,15 +1524,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_2994/3077675025.py:49: DeprecationWarning:\n",
+      "/tmp/ipykernel_2930/3077675025.py:49: DeprecationWarning:\n",
       "\n",
       "`where` is deprecated. Use `filter` instead.\n",
       "\n",
-      "/tmp/ipykernel_2994/3077675025.py:51: DeprecationWarning:\n",
+      "/tmp/ipykernel_2930/3077675025.py:51: DeprecationWarning:\n",
       "\n",
       "`where` is deprecated. Use `filter` instead.\n",
       "\n",
-      "/tmp/ipykernel_2994/3077675025.py:52: DeprecationWarning:\n",
+      "/tmp/ipykernel_2930/3077675025.py:52: DeprecationWarning:\n",
       "\n",
       "`where` is deprecated. Use `filter` instead.\n",
       "\n"
@@ -1542,19 +1542,19 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_877ee_row0_col4, #T_877ee_row1_col4, #T_877ee_row2_col4, #T_877ee_row3_col4, #T_877ee_row4_col4, #T_877ee_row5_col4, #T_877ee_row6_col4, #T_877ee_row7_col4, #T_877ee_row8_col4, #T_877ee_row9_col4, #T_877ee_row10_col4, #T_877ee_row11_col4, #T_877ee_row12_col4, #T_877ee_row13_col4, #T_877ee_row14_col4, #T_877ee_row15_col4, #T_877ee_row16_col4, #T_877ee_row17_col4, #T_877ee_row18_col4, #T_877ee_row19_col4, #T_877ee_row20_col4, #T_877ee_row21_col4, #T_877ee_row22_col4, #T_877ee_row23_col4, #T_877ee_row24_col4, #T_877ee_row25_col4, #T_877ee_row26_col4, #T_877ee_row27_col4, #T_877ee_row28_col4, #T_877ee_row29_col4, #T_877ee_row30_col4, #T_877ee_row31_col4, #T_877ee_row32_col4, #T_877ee_row33_col4, #T_877ee_row34_col4, #T_877ee_row35_col4, #T_877ee_row36_col4 {\n",
+       "#T_85bcd_row0_col4, #T_85bcd_row1_col4, #T_85bcd_row2_col4, #T_85bcd_row3_col4, #T_85bcd_row4_col4, #T_85bcd_row5_col4, #T_85bcd_row6_col4, #T_85bcd_row7_col4, #T_85bcd_row8_col4, #T_85bcd_row9_col4, #T_85bcd_row10_col4, #T_85bcd_row11_col4, #T_85bcd_row12_col4, #T_85bcd_row13_col4, #T_85bcd_row14_col4, #T_85bcd_row15_col4, #T_85bcd_row16_col4, #T_85bcd_row17_col4, #T_85bcd_row18_col4, #T_85bcd_row19_col4, #T_85bcd_row20_col4, #T_85bcd_row21_col4, #T_85bcd_row22_col4, #T_85bcd_row23_col4, #T_85bcd_row24_col4, #T_85bcd_row25_col4, #T_85bcd_row26_col4, #T_85bcd_row27_col4, #T_85bcd_row28_col4, #T_85bcd_row29_col4, #T_85bcd_row30_col4, #T_85bcd_row31_col4, #T_85bcd_row32_col4, #T_85bcd_row33_col4, #T_85bcd_row34_col4, #T_85bcd_row35_col4, #T_85bcd_row36_col4 {\n",
        "  color: blue;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_877ee\">\n",
+       "<table id=\"T_85bcd\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_877ee_level0_col0\" class=\"col_heading level0 col0\" >Value</th>\n",
-       "      <th id=\"T_877ee_level0_col1\" class=\"col_heading level0 col1\" >Bin</th>\n",
-       "      <th id=\"T_877ee_level0_col2\" class=\"col_heading level0 col2\" >Positives</th>\n",
-       "      <th id=\"T_877ee_level0_col3\" class=\"col_heading level0 col3\" >Negatives</th>\n",
-       "      <th id=\"T_877ee_level0_col4\" class=\"col_heading level0 col4\" >LogOdds</th>\n",
+       "      <th id=\"T_85bcd_level0_col0\" class=\"col_heading level0 col0\" >Value</th>\n",
+       "      <th id=\"T_85bcd_level0_col1\" class=\"col_heading level0 col1\" >Bin</th>\n",
+       "      <th id=\"T_85bcd_level0_col2\" class=\"col_heading level0 col2\" >Positives</th>\n",
+       "      <th id=\"T_85bcd_level0_col3\" class=\"col_heading level0 col3\" >Negatives</th>\n",
+       "      <th id=\"T_85bcd_level0_col4\" class=\"col_heading level0 col4\" >LogOdds</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th class=\"index_name level0\" >PredictorName</th>\n",
@@ -1567,306 +1567,306 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row0\" class=\"row_heading level0 row0\" >Customer.Age</th>\n",
-       "      <td id=\"T_877ee_row0_col0\" class=\"data row0 col0\" >34.56</td>\n",
-       "      <td id=\"T_877ee_row0_col1\" class=\"data row0 col1\" >4.000000</td>\n",
-       "      <td id=\"T_877ee_row0_col2\" class=\"data row0 col2\" >9.000000</td>\n",
-       "      <td id=\"T_877ee_row0_col3\" class=\"data row0 col3\" >198.000000</td>\n",
-       "      <td id=\"T_877ee_row0_col4\" class=\"data row0 col4\" >-1.145923</td>\n",
+       "      <th id=\"T_85bcd_level0_row0\" class=\"row_heading level0 row0\" >Customer.Age</th>\n",
+       "      <td id=\"T_85bcd_row0_col0\" class=\"data row0 col0\" >34.56</td>\n",
+       "      <td id=\"T_85bcd_row0_col1\" class=\"data row0 col1\" >4.000000</td>\n",
+       "      <td id=\"T_85bcd_row0_col2\" class=\"data row0 col2\" >9.000000</td>\n",
+       "      <td id=\"T_85bcd_row0_col3\" class=\"data row0 col3\" >198.000000</td>\n",
+       "      <td id=\"T_85bcd_row0_col4\" class=\"data row0 col4\" >-1.145923</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row1\" class=\"row_heading level0 row1\" >Customer.AnnualIncome</th>\n",
-       "      <td id=\"T_877ee_row1_col0\" class=\"data row1 col0\" >-24043.049</td>\n",
-       "      <td id=\"T_877ee_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
-       "      <td id=\"T_877ee_row1_col2\" class=\"data row1 col2\" >74.000000</td>\n",
-       "      <td id=\"T_877ee_row1_col3\" class=\"data row1 col3\" >1166.000000</td>\n",
-       "      <td id=\"T_877ee_row1_col4\" class=\"data row1 col4\" >-0.819651</td>\n",
+       "      <th id=\"T_85bcd_level0_row1\" class=\"row_heading level0 row1\" >Customer.AnnualIncome</th>\n",
+       "      <td id=\"T_85bcd_row1_col0\" class=\"data row1 col0\" >-24043.049</td>\n",
+       "      <td id=\"T_85bcd_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
+       "      <td id=\"T_85bcd_row1_col2\" class=\"data row1 col2\" >74.000000</td>\n",
+       "      <td id=\"T_85bcd_row1_col3\" class=\"data row1 col3\" >1166.000000</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row2\" class=\"row_heading level0 row2\" >Customer.BusinessSegment</th>\n",
-       "      <td id=\"T_877ee_row2_col0\" class=\"data row2 col0\" >middleSegmentPlus</td>\n",
-       "      <td id=\"T_877ee_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
-       "      <td id=\"T_877ee_row2_col2\" class=\"data row2 col2\" >96.000000</td>\n",
-       "      <td id=\"T_877ee_row2_col3\" class=\"data row2 col3\" >970.000000</td>\n",
-       "      <td id=\"T_877ee_row2_col4\" class=\"data row2 col4\" >-0.376415</td>\n",
+       "      <th id=\"T_85bcd_level0_row2\" class=\"row_heading level0 row2\" >Customer.BusinessSegment</th>\n",
+       "      <td id=\"T_85bcd_row2_col0\" class=\"data row2 col0\" >middleSegmentPlus</td>\n",
+       "      <td id=\"T_85bcd_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
+       "      <td id=\"T_85bcd_row2_col2\" class=\"data row2 col2\" >96.000000</td>\n",
+       "      <td id=\"T_85bcd_row2_col3\" class=\"data row2 col3\" >970.000000</td>\n",
+       "      <td id=\"T_85bcd_row2_col4\" class=\"data row2 col4\" >-0.376415</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row3\" class=\"row_heading level0 row3\" >Customer.CLV</th>\n",
-       "      <td id=\"T_877ee_row3_col0\" class=\"data row3 col0\" >NON-MISSING</td>\n",
-       "      <td id=\"T_877ee_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
-       "      <td id=\"T_877ee_row3_col2\" class=\"data row3 col2\" >111.000000</td>\n",
-       "      <td id=\"T_877ee_row3_col3\" class=\"data row3 col3\" >570.000000</td>\n",
-       "      <td id=\"T_877ee_row3_col4\" class=\"data row3 col4\" >0.300922</td>\n",
+       "      <th id=\"T_85bcd_level0_row3\" class=\"row_heading level0 row3\" >Customer.CLV</th>\n",
+       "      <td id=\"T_85bcd_row3_col0\" class=\"data row3 col0\" >NON-MISSING</td>\n",
+       "      <td id=\"T_85bcd_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
+       "      <td id=\"T_85bcd_row3_col2\" class=\"data row3 col2\" >111.000000</td>\n",
+       "      <td id=\"T_85bcd_row3_col3\" class=\"data row3 col3\" >570.000000</td>\n",
+       "      <td id=\"T_85bcd_row3_col4\" class=\"data row3 col4\" >0.300922</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row4\" class=\"row_heading level0 row4\" >Customer.CLV_VALUE</th>\n",
-       "      <td id=\"T_877ee_row4_col0\" class=\"data row4 col0\" >1345.52</td>\n",
-       "      <td id=\"T_877ee_row4_col1\" class=\"data row4 col1\" >4.000000</td>\n",
-       "      <td id=\"T_877ee_row4_col2\" class=\"data row4 col2\" >31.000000</td>\n",
-       "      <td id=\"T_877ee_row4_col3\" class=\"data row4 col3\" >297.000000</td>\n",
-       "      <td id=\"T_877ee_row4_col4\" class=\"data row4 col4\" >-0.322731</td>\n",
+       "      <th id=\"T_85bcd_level0_row4\" class=\"row_heading level0 row4\" >Customer.CLV_VALUE</th>\n",
+       "      <td id=\"T_85bcd_row4_col0\" class=\"data row4 col0\" >1345.52</td>\n",
+       "      <td id=\"T_85bcd_row4_col1\" class=\"data row4 col1\" >4.000000</td>\n",
+       "      <td id=\"T_85bcd_row4_col2\" class=\"data row4 col2\" >31.000000</td>\n",
+       "      <td id=\"T_85bcd_row4_col3\" class=\"data row4 col3\" >297.000000</td>\n",
+       "      <td id=\"T_85bcd_row4_col4\" class=\"data row4 col4\" >-0.322731</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row5\" class=\"row_heading level0 row5\" >Customer.CreditScore</th>\n",
-       "      <td id=\"T_877ee_row5_col0\" class=\"data row5 col0\" >518.92</td>\n",
-       "      <td id=\"T_877ee_row5_col1\" class=\"data row5 col1\" >3.000000</td>\n",
-       "      <td id=\"T_877ee_row5_col2\" class=\"data row5 col2\" >33.000000</td>\n",
-       "      <td id=\"T_877ee_row5_col3\" class=\"data row5 col3\" >205.000000</td>\n",
-       "      <td id=\"T_877ee_row5_col4\" class=\"data row5 col4\" >0.110531</td>\n",
+       "      <th id=\"T_85bcd_level0_row5\" class=\"row_heading level0 row5\" >Customer.CreditScore</th>\n",
+       "      <td id=\"T_85bcd_row5_col0\" class=\"data row5 col0\" >518.92</td>\n",
+       "      <td id=\"T_85bcd_row5_col1\" class=\"data row5 col1\" >3.000000</td>\n",
+       "      <td id=\"T_85bcd_row5_col2\" class=\"data row5 col2\" >33.000000</td>\n",
+       "      <td id=\"T_85bcd_row5_col3\" class=\"data row5 col3\" >205.000000</td>\n",
+       "      <td id=\"T_85bcd_row5_col4\" class=\"data row5 col4\" >0.110531</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row6\" class=\"row_heading level0 row6\" >Customer.Date_of_Birth</th>\n",
-       "      <td id=\"T_877ee_row6_col0\" class=\"data row6 col0\" >18773.504</td>\n",
-       "      <td id=\"T_877ee_row6_col1\" class=\"data row6 col1\" >5.000000</td>\n",
-       "      <td id=\"T_877ee_row6_col2\" class=\"data row6 col2\" >28.000000</td>\n",
-       "      <td id=\"T_877ee_row6_col3\" class=\"data row6 col3\" >152.000000</td>\n",
-       "      <td id=\"T_877ee_row6_col4\" class=\"data row6 col4\" >0.244642</td>\n",
+       "      <th id=\"T_85bcd_level0_row6\" class=\"row_heading level0 row6\" >Customer.Date_of_Birth</th>\n",
+       "      <td id=\"T_85bcd_row6_col0\" class=\"data row6 col0\" >18773.504</td>\n",
+       "      <td id=\"T_85bcd_row6_col1\" class=\"data row6 col1\" >5.000000</td>\n",
+       "      <td id=\"T_85bcd_row6_col2\" class=\"data row6 col2\" >28.000000</td>\n",
+       "      <td id=\"T_85bcd_row6_col3\" class=\"data row6 col3\" >152.000000</td>\n",
+       "      <td id=\"T_85bcd_row6_col4\" class=\"data row6 col4\" >0.244642</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row7\" class=\"row_heading level0 row7\" >Customer.Gender</th>\n",
-       "      <td id=\"T_877ee_row7_col0\" class=\"data row7 col0\" >U</td>\n",
-       "      <td id=\"T_877ee_row7_col1\" class=\"data row7 col1\" >1.000000</td>\n",
-       "      <td id=\"T_877ee_row7_col2\" class=\"data row7 col2\" >52.000000</td>\n",
-       "      <td id=\"T_877ee_row7_col3\" class=\"data row7 col3\" >481.000000</td>\n",
-       "      <td id=\"T_877ee_row7_col4\" class=\"data row7 col4\" >-0.285516</td>\n",
+       "      <th id=\"T_85bcd_level0_row7\" class=\"row_heading level0 row7\" >Customer.Gender</th>\n",
+       "      <td id=\"T_85bcd_row7_col0\" class=\"data row7 col0\" >U</td>\n",
+       "      <td id=\"T_85bcd_row7_col1\" class=\"data row7 col1\" >1.000000</td>\n",
+       "      <td id=\"T_85bcd_row7_col2\" class=\"data row7 col2\" >52.000000</td>\n",
+       "      <td id=\"T_85bcd_row7_col3\" class=\"data row7 col3\" >481.000000</td>\n",
+       "      <td id=\"T_85bcd_row7_col4\" class=\"data row7 col4\" >-0.285516</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row8\" class=\"row_heading level0 row8\" >Customer.MaritalStatus</th>\n",
-       "      <td id=\"T_877ee_row8_col0\" class=\"data row8 col0\" >No Resp+</td>\n",
-       "      <td id=\"T_877ee_row8_col1\" class=\"data row8 col1\" >1.000000</td>\n",
-       "      <td id=\"T_877ee_row8_col2\" class=\"data row8 col2\" >67.000000</td>\n",
-       "      <td id=\"T_877ee_row8_col3\" class=\"data row8 col3\" >745.000000</td>\n",
-       "      <td id=\"T_877ee_row8_col4\" class=\"data row8 col4\" >-0.470766</td>\n",
+       "      <th id=\"T_85bcd_level0_row8\" class=\"row_heading level0 row8\" >Customer.MaritalStatus</th>\n",
+       "      <td id=\"T_85bcd_row8_col0\" class=\"data row8 col0\" >No Resp+</td>\n",
+       "      <td id=\"T_85bcd_row8_col1\" class=\"data row8 col1\" >1.000000</td>\n",
+       "      <td id=\"T_85bcd_row8_col2\" class=\"data row8 col2\" >67.000000</td>\n",
+       "      <td id=\"T_85bcd_row8_col3\" class=\"data row8 col3\" >745.000000</td>\n",
+       "      <td id=\"T_85bcd_row8_col4\" class=\"data row8 col4\" >-0.470766</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row9\" class=\"row_heading level0 row9\" >Customer.NetWealth</th>\n",
-       "      <td id=\"T_877ee_row9_col0\" class=\"data row9 col0\" >17992.398</td>\n",
-       "      <td id=\"T_877ee_row9_col1\" class=\"data row9 col1\" >4.000000</td>\n",
-       "      <td id=\"T_877ee_row9_col2\" class=\"data row9 col2\" >51.000000</td>\n",
-       "      <td id=\"T_877ee_row9_col3\" class=\"data row9 col3\" >179.000000</td>\n",
-       "      <td id=\"T_877ee_row9_col4\" class=\"data row9 col4\" >0.679600</td>\n",
+       "      <th id=\"T_85bcd_level0_row9\" class=\"row_heading level0 row9\" >Customer.NetWealth</th>\n",
+       "      <td id=\"T_85bcd_row9_col0\" class=\"data row9 col0\" >17992.398</td>\n",
+       "      <td id=\"T_85bcd_row9_col1\" class=\"data row9 col1\" >4.000000</td>\n",
+       "      <td id=\"T_85bcd_row9_col2\" class=\"data row9 col2\" >51.000000</td>\n",
+       "      <td id=\"T_85bcd_row9_col3\" class=\"data row9 col3\" >179.000000</td>\n",
+       "      <td id=\"T_85bcd_row9_col4\" class=\"data row9 col4\" >0.679600</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row10\" class=\"row_heading level0 row10\" >Customer.NoOfDependents</th>\n",
-       "      <td id=\"T_877ee_row10_col0\" class=\"data row10 col0\" >0.0</td>\n",
-       "      <td id=\"T_877ee_row10_col1\" class=\"data row10 col1\" >1.000000</td>\n",
-       "      <td id=\"T_877ee_row10_col2\" class=\"data row10 col2\" >111.000000</td>\n",
-       "      <td id=\"T_877ee_row10_col3\" class=\"data row10 col3\" >850.000000</td>\n",
-       "      <td id=\"T_877ee_row10_col4\" class=\"data row10 col4\" >-0.099690</td>\n",
+       "      <th id=\"T_85bcd_level0_row10\" class=\"row_heading level0 row10\" >Customer.NoOfDependents</th>\n",
+       "      <td id=\"T_85bcd_row10_col0\" class=\"data row10 col0\" >0.0</td>\n",
+       "      <td id=\"T_85bcd_row10_col1\" class=\"data row10 col1\" >1.000000</td>\n",
+       "      <td id=\"T_85bcd_row10_col2\" class=\"data row10 col2\" >111.000000</td>\n",
+       "      <td id=\"T_85bcd_row10_col3\" class=\"data row10 col3\" >850.000000</td>\n",
+       "      <td id=\"T_85bcd_row10_col4\" class=\"data row10 col4\" >-0.099690</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row11\" class=\"row_heading level0 row11\" >Customer.Prefix</th>\n",
-       "      <td id=\"T_877ee_row11_col0\" class=\"data row11 col0\" >Mrs.</td>\n",
-       "      <td id=\"T_877ee_row11_col1\" class=\"data row11 col1\" >1.000000</td>\n",
-       "      <td id=\"T_877ee_row11_col2\" class=\"data row11 col2\" >64.000000</td>\n",
-       "      <td id=\"T_877ee_row11_col3\" class=\"data row11 col3\" >552.000000</td>\n",
-       "      <td id=\"T_877ee_row11_col4\" class=\"data row11 col4\" >-0.216664</td>\n",
+       "      <th id=\"T_85bcd_level0_row11\" class=\"row_heading level0 row11\" >Customer.Prefix</th>\n",
+       "      <td id=\"T_85bcd_row11_col0\" class=\"data row11 col0\" >Mrs.</td>\n",
+       "      <td id=\"T_85bcd_row11_col1\" class=\"data row11 col1\" >1.000000</td>\n",
+       "      <td id=\"T_85bcd_row11_col2\" class=\"data row11 col2\" >64.000000</td>\n",
+       "      <td id=\"T_85bcd_row11_col3\" class=\"data row11 col3\" >552.000000</td>\n",
+       "      <td id=\"T_85bcd_row11_col4\" class=\"data row11 col4\" >-0.216664</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row12\" class=\"row_heading level0 row12\" >Customer.RelationshipStartDate</th>\n",
-       "      <td id=\"T_877ee_row12_col0\" class=\"data row12 col0\" >1426.4596</td>\n",
-       "      <td id=\"T_877ee_row12_col1\" class=\"data row12 col1\" >4.000000</td>\n",
-       "      <td id=\"T_877ee_row12_col2\" class=\"data row12 col2\" >16.000000</td>\n",
-       "      <td id=\"T_877ee_row12_col3\" class=\"data row12 col3\" >117.000000</td>\n",
-       "      <td id=\"T_877ee_row12_col4\" class=\"data row12 col4\" >-0.050204</td>\n",
+       "      <th id=\"T_85bcd_level0_row12\" class=\"row_heading level0 row12\" >Customer.RelationshipStartDate</th>\n",
+       "      <td id=\"T_85bcd_row12_col0\" class=\"data row12 col0\" >1426.4596</td>\n",
+       "      <td id=\"T_85bcd_row12_col1\" class=\"data row12 col1\" >4.000000</td>\n",
+       "      <td id=\"T_85bcd_row12_col2\" class=\"data row12 col2\" >16.000000</td>\n",
+       "      <td id=\"T_85bcd_row12_col3\" class=\"data row12 col3\" >117.000000</td>\n",
+       "      <td id=\"T_85bcd_row12_col4\" class=\"data row12 col4\" >-0.050204</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row13\" class=\"row_heading level0 row13\" >Customer.RiskCode</th>\n",
-       "      <td id=\"T_877ee_row13_col0\" class=\"data row13 col0\" >R4</td>\n",
-       "      <td id=\"T_877ee_row13_col1\" class=\"data row13 col1\" >1.000000</td>\n",
-       "      <td id=\"T_877ee_row13_col2\" class=\"data row13 col2\" >36.000000</td>\n",
-       "      <td id=\"T_877ee_row13_col3\" class=\"data row13 col3\" >329.000000</td>\n",
-       "      <td id=\"T_877ee_row13_col4\" class=\"data row13 col4\" >-0.270925</td>\n",
+       "      <th id=\"T_85bcd_level0_row13\" class=\"row_heading level0 row13\" >Customer.RiskCode</th>\n",
+       "      <td id=\"T_85bcd_row13_col0\" class=\"data row13 col0\" >R4</td>\n",
+       "      <td id=\"T_85bcd_row13_col1\" class=\"data row13 col1\" >1.000000</td>\n",
+       "      <td id=\"T_85bcd_row13_col2\" class=\"data row13 col2\" >36.000000</td>\n",
+       "      <td id=\"T_85bcd_row13_col3\" class=\"data row13 col3\" >329.000000</td>\n",
+       "      <td id=\"T_85bcd_row13_col4\" class=\"data row13 col4\" >-0.270925</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row14\" class=\"row_heading level0 row14\" >Customer.WinScore</th>\n",
-       "      <td id=\"T_877ee_row14_col0\" class=\"data row14 col0\" >66.600006</td>\n",
-       "      <td id=\"T_877ee_row14_col1\" class=\"data row14 col1\" >4.000000</td>\n",
-       "      <td id=\"T_877ee_row14_col2\" class=\"data row14 col2\" >39.000000</td>\n",
-       "      <td id=\"T_877ee_row14_col3\" class=\"data row14 col3\" >102.000000</td>\n",
-       "      <td id=\"T_877ee_row14_col4\" class=\"data row14 col4\" >0.973755</td>\n",
+       "      <th id=\"T_85bcd_level0_row14\" class=\"row_heading level0 row14\" >Customer.WinScore</th>\n",
+       "      <td id=\"T_85bcd_row14_col0\" class=\"data row14 col0\" >66.600006</td>\n",
+       "      <td id=\"T_85bcd_row14_col1\" class=\"data row14 col1\" >4.000000</td>\n",
+       "      <td id=\"T_85bcd_row14_col2\" class=\"data row14 col2\" >39.000000</td>\n",
+       "      <td id=\"T_85bcd_row14_col3\" class=\"data row14 col3\" >102.000000</td>\n",
+       "      <td id=\"T_85bcd_row14_col4\" class=\"data row14 col4\" >0.973755</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row15\" class=\"row_heading level0 row15\" >Customer.pyCountry</th>\n",
-       "      <td id=\"T_877ee_row15_col0\" class=\"data row15 col0\" >USA</td>\n",
-       "      <td id=\"T_877ee_row15_col1\" class=\"data row15 col1\" >1.000000</td>\n",
-       "      <td id=\"T_877ee_row15_col2\" class=\"data row15 col2\" >99.000000</td>\n",
-       "      <td id=\"T_877ee_row15_col3\" class=\"data row15 col3\" >776.000000</td>\n",
-       "      <td id=\"T_877ee_row15_col4\" class=\"data row15 col4\" >-0.122691</td>\n",
+       "      <th id=\"T_85bcd_level0_row15\" class=\"row_heading level0 row15\" >Customer.pyCountry</th>\n",
+       "      <td id=\"T_85bcd_row15_col0\" class=\"data row15 col0\" >USA</td>\n",
+       "      <td id=\"T_85bcd_row15_col1\" class=\"data row15 col1\" >1.000000</td>\n",
+       "      <td id=\"T_85bcd_row15_col2\" class=\"data row15 col2\" >99.000000</td>\n",
+       "      <td id=\"T_85bcd_row15_col3\" class=\"data row15 col3\" >776.000000</td>\n",
+       "      <td id=\"T_85bcd_row15_col4\" class=\"data row15 col4\" >-0.122691</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row16\" class=\"row_heading level0 row16\" >IH.Email.Outbound.Accepted.pxLastGroupID</th>\n",
-       "      <td id=\"T_877ee_row16_col0\" class=\"data row16 col0\" >HomeLoans</td>\n",
-       "      <td id=\"T_877ee_row16_col1\" class=\"data row16 col1\" >3.000000</td>\n",
-       "      <td id=\"T_877ee_row16_col2\" class=\"data row16 col2\" >25.000000</td>\n",
-       "      <td id=\"T_877ee_row16_col3\" class=\"data row16 col3\" >218.000000</td>\n",
-       "      <td id=\"T_877ee_row16_col4\" class=\"data row16 col4\" >-0.227166</td>\n",
+       "      <th id=\"T_85bcd_level0_row16\" class=\"row_heading level0 row16\" >IH.Email.Outbound.Accepted.pxLastGroupID</th>\n",
+       "      <td id=\"T_85bcd_row16_col0\" class=\"data row16 col0\" >HomeLoans</td>\n",
+       "      <td id=\"T_85bcd_row16_col1\" class=\"data row16 col1\" >3.000000</td>\n",
+       "      <td id=\"T_85bcd_row16_col2\" class=\"data row16 col2\" >25.000000</td>\n",
+       "      <td id=\"T_85bcd_row16_col3\" class=\"data row16 col3\" >218.000000</td>\n",
+       "      <td id=\"T_85bcd_row16_col4\" class=\"data row16 col4\" >-0.227166</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row17\" class=\"row_heading level0 row17\" >IH.Email.Outbound.Accepted.pxLastOutcomeTime.DaysSince</th>\n",
-       "      <td id=\"T_877ee_row17_col0\" class=\"data row17 col0\" >-55.88436</td>\n",
-       "      <td id=\"T_877ee_row17_col1\" class=\"data row17 col1\" >2.000000</td>\n",
-       "      <td id=\"T_877ee_row17_col2\" class=\"data row17 col2\" >145.000000</td>\n",
-       "      <td id=\"T_877ee_row17_col3\" class=\"data row17 col3\" >881.000000</td>\n",
-       "      <td id=\"T_877ee_row17_col4\" class=\"data row17 col4\" >0.130525</td>\n",
+       "      <th id=\"T_85bcd_level0_row17\" class=\"row_heading level0 row17\" >IH.Email.Outbound.Accepted.pxLastOutcomeTime.DaysSince</th>\n",
+       "      <td id=\"T_85bcd_row17_col0\" class=\"data row17 col0\" >-55.88436</td>\n",
+       "      <td id=\"T_85bcd_row17_col1\" class=\"data row17 col1\" >2.000000</td>\n",
+       "      <td id=\"T_85bcd_row17_col2\" class=\"data row17 col2\" >145.000000</td>\n",
+       "      <td id=\"T_85bcd_row17_col3\" class=\"data row17 col3\" >881.000000</td>\n",
+       "      <td id=\"T_85bcd_row17_col4\" class=\"data row17 col4\" >0.130525</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row18\" class=\"row_heading level0 row18\" >IH.Email.Outbound.Accepted.pyHistoricalOutcomeCount</th>\n",
-       "      <td id=\"T_877ee_row18_col0\" class=\"data row18 col0\" >1.5</td>\n",
-       "      <td id=\"T_877ee_row18_col1\" class=\"data row18 col1\" >2.000000</td>\n",
-       "      <td id=\"T_877ee_row18_col2\" class=\"data row18 col2\" >30.000000</td>\n",
-       "      <td id=\"T_877ee_row18_col3\" class=\"data row18 col3\" >351.000000</td>\n",
-       "      <td id=\"T_877ee_row18_col4\" class=\"data row18 col4\" >-0.520104</td>\n",
+       "      <th id=\"T_85bcd_level0_row18\" class=\"row_heading level0 row18\" >IH.Email.Outbound.Accepted.pyHistoricalOutcomeCount</th>\n",
+       "      <td id=\"T_85bcd_row18_col0\" class=\"data row18 col0\" >1.5</td>\n",
+       "      <td id=\"T_85bcd_row18_col1\" class=\"data row18 col1\" >2.000000</td>\n",
+       "      <td id=\"T_85bcd_row18_col2\" class=\"data row18 col2\" >30.000000</td>\n",
+       "      <td id=\"T_85bcd_row18_col3\" class=\"data row18 col3\" >351.000000</td>\n",
+       "      <td id=\"T_85bcd_row18_col4\" class=\"data row18 col4\" >-0.520104</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row19\" class=\"row_heading level0 row19\" >IH.Email.Outbound.Churned.pyHistoricalOutcomeCount</th>\n",
-       "      <td id=\"T_877ee_row19_col0\" class=\"data row19 col0\" >None</td>\n",
-       "      <td id=\"T_877ee_row19_col1\" class=\"data row19 col1\" >1.000000</td>\n",
-       "      <td id=\"T_877ee_row19_col2\" class=\"data row19 col2\" >143.000000</td>\n",
-       "      <td id=\"T_877ee_row19_col3\" class=\"data row19 col3\" >898.000000</td>\n",
-       "      <td id=\"T_877ee_row19_col4\" class=\"data row19 col4\" >0.101486</td>\n",
+       "      <th id=\"T_85bcd_level0_row19\" class=\"row_heading level0 row19\" >IH.Email.Outbound.Churned.pyHistoricalOutcomeCount</th>\n",
+       "      <td id=\"T_85bcd_row19_col0\" class=\"data row19 col0\" >None</td>\n",
+       "      <td id=\"T_85bcd_row19_col1\" class=\"data row19 col1\" >1.000000</td>\n",
+       "      <td id=\"T_85bcd_row19_col2\" class=\"data row19 col2\" >143.000000</td>\n",
+       "      <td id=\"T_85bcd_row19_col3\" class=\"data row19 col3\" >898.000000</td>\n",
+       "      <td id=\"T_85bcd_row19_col4\" class=\"data row19 col4\" >0.101486</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row20\" class=\"row_heading level0 row20\" >IH.Email.Outbound.Loyal.pxLastOutcomeTime.DaysSince</th>\n",
-       "      <td id=\"T_877ee_row20_col0\" class=\"data row20 col0\" >None</td>\n",
-       "      <td id=\"T_877ee_row20_col1\" class=\"data row20 col1\" >1.000000</td>\n",
-       "      <td id=\"T_877ee_row20_col2\" class=\"data row20 col2\" >129.000000</td>\n",
-       "      <td id=\"T_877ee_row20_col3\" class=\"data row20 col3\" >1071.000000</td>\n",
-       "      <td id=\"T_877ee_row20_col4\" class=\"data row20 col4\" >-0.178813</td>\n",
+       "      <th id=\"T_85bcd_level0_row20\" class=\"row_heading level0 row20\" >IH.Email.Outbound.Loyal.pxLastOutcomeTime.DaysSince</th>\n",
+       "      <td id=\"T_85bcd_row20_col0\" class=\"data row20 col0\" >None</td>\n",
+       "      <td id=\"T_85bcd_row20_col1\" class=\"data row20 col1\" >1.000000</td>\n",
+       "      <td id=\"T_85bcd_row20_col2\" class=\"data row20 col2\" >129.000000</td>\n",
+       "      <td id=\"T_85bcd_row20_col3\" class=\"data row20 col3\" >1071.000000</td>\n",
+       "      <td id=\"T_85bcd_row20_col4\" class=\"data row20 col4\" >-0.178813</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row21\" class=\"row_heading level0 row21\" >IH.Email.Outbound.Rejected.pyHistoricalOutcomeCount</th>\n",
-       "      <td id=\"T_877ee_row21_col0\" class=\"data row21 col0\" >83.16</td>\n",
-       "      <td id=\"T_877ee_row21_col1\" class=\"data row21 col1\" >3.000000</td>\n",
-       "      <td id=\"T_877ee_row21_col2\" class=\"data row21 col2\" >24.000000</td>\n",
-       "      <td id=\"T_877ee_row21_col3\" class=\"data row21 col3\" >218.000000</td>\n",
-       "      <td id=\"T_877ee_row21_col4\" class=\"data row21 col4\" >-0.267751</td>\n",
+       "      <th id=\"T_85bcd_level0_row21\" class=\"row_heading level0 row21\" >IH.Email.Outbound.Rejected.pyHistoricalOutcomeCount</th>\n",
+       "      <td id=\"T_85bcd_row21_col0\" class=\"data row21 col0\" >83.16</td>\n",
+       "      <td id=\"T_85bcd_row21_col1\" class=\"data row21 col1\" >3.000000</td>\n",
+       "      <td id=\"T_85bcd_row21_col2\" class=\"data row21 col2\" >24.000000</td>\n",
+       "      <td id=\"T_85bcd_row21_col3\" class=\"data row21 col3\" >218.000000</td>\n",
+       "      <td id=\"T_85bcd_row21_col4\" class=\"data row21 col4\" >-0.267751</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row22\" class=\"row_heading level0 row22\" >IH.SMS.Outbound.Accepted.pxLastGroupID</th>\n",
-       "      <td id=\"T_877ee_row22_col0\" class=\"data row22 col0\" >Account</td>\n",
-       "      <td id=\"T_877ee_row22_col1\" class=\"data row22 col1\" >4.000000</td>\n",
-       "      <td id=\"T_877ee_row22_col2\" class=\"data row22 col2\" >45.000000</td>\n",
-       "      <td id=\"T_877ee_row22_col3\" class=\"data row22 col3\" >316.000000</td>\n",
-       "      <td id=\"T_877ee_row22_col4\" class=\"data row22 col4\" >-0.013291</td>\n",
+       "      <th id=\"T_85bcd_level0_row22\" class=\"row_heading level0 row22\" >IH.SMS.Outbound.Accepted.pxLastGroupID</th>\n",
+       "      <td id=\"T_85bcd_row22_col0\" class=\"data row22 col0\" >Account</td>\n",
+       "      <td id=\"T_85bcd_row22_col1\" class=\"data row22 col1\" >4.000000</td>\n",
+       "      <td id=\"T_85bcd_row22_col2\" class=\"data row22 col2\" >45.000000</td>\n",
+       "      <td id=\"T_85bcd_row22_col3\" class=\"data row22 col3\" >316.000000</td>\n",
+       "      <td id=\"T_85bcd_row22_col4\" class=\"data row22 col4\" >-0.013291</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row23\" class=\"row_heading level0 row23\" >IH.SMS.Outbound.Accepted.pyHistoricalOutcomeCount</th>\n",
-       "      <td id=\"T_877ee_row23_col0\" class=\"data row23 col0\" >9.02</td>\n",
-       "      <td id=\"T_877ee_row23_col1\" class=\"data row23 col1\" >4.000000</td>\n",
-       "      <td id=\"T_877ee_row23_col2\" class=\"data row23 col2\" >6.000000</td>\n",
-       "      <td id=\"T_877ee_row23_col3\" class=\"data row23 col3\" >96.000000</td>\n",
-       "      <td id=\"T_877ee_row23_col4\" class=\"data row23 col4\" >-0.821986</td>\n",
+       "      <th id=\"T_85bcd_level0_row23\" class=\"row_heading level0 row23\" >IH.SMS.Outbound.Accepted.pyHistoricalOutcomeCount</th>\n",
+       "      <td id=\"T_85bcd_row23_col0\" class=\"data row23 col0\" >9.02</td>\n",
+       "      <td id=\"T_85bcd_row23_col1\" class=\"data row23 col1\" >4.000000</td>\n",
+       "      <td id=\"T_85bcd_row23_col2\" class=\"data row23 col2\" >6.000000</td>\n",
+       "      <td id=\"T_85bcd_row23_col3\" class=\"data row23 col3\" >96.000000</td>\n",
+       "      <td id=\"T_85bcd_row23_col4\" class=\"data row23 col4\" >-0.821986</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row24\" class=\"row_heading level0 row24\" >IH.SMS.Outbound.Churned.pxLastOutcomeTime.DaysSince</th>\n",
-       "      <td id=\"T_877ee_row24_col0\" class=\"data row24 col0\" >-20.5537</td>\n",
-       "      <td id=\"T_877ee_row24_col1\" class=\"data row24 col1\" >2.000000</td>\n",
-       "      <td id=\"T_877ee_row24_col2\" class=\"data row24 col2\" >9.000000</td>\n",
-       "      <td id=\"T_877ee_row24_col3\" class=\"data row24 col3\" >27.000000</td>\n",
-       "      <td id=\"T_877ee_row24_col4\" class=\"data row24 col4\" >0.849243</td>\n",
+       "      <th id=\"T_85bcd_level0_row24\" class=\"row_heading level0 row24\" >IH.SMS.Outbound.Churned.pxLastOutcomeTime.DaysSince</th>\n",
+       "      <td id=\"T_85bcd_row24_col0\" class=\"data row24 col0\" >-20.5537</td>\n",
+       "      <td id=\"T_85bcd_row24_col1\" class=\"data row24 col1\" >2.000000</td>\n",
+       "      <td id=\"T_85bcd_row24_col2\" class=\"data row24 col2\" >9.000000</td>\n",
+       "      <td id=\"T_85bcd_row24_col3\" class=\"data row24 col3\" >27.000000</td>\n",
+       "      <td id=\"T_85bcd_row24_col4\" class=\"data row24 col4\" >0.849243</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row25\" class=\"row_heading level0 row25\" >IH.SMS.Outbound.Loyal.pxLastOutcomeTime.DaysSince</th>\n",
-       "      <td id=\"T_877ee_row25_col0\" class=\"data row25 col0\" >None</td>\n",
-       "      <td id=\"T_877ee_row25_col1\" class=\"data row25 col1\" >1.000000</td>\n",
-       "      <td id=\"T_877ee_row25_col2\" class=\"data row25 col2\" >165.000000</td>\n",
-       "      <td id=\"T_877ee_row25_col3\" class=\"data row25 col3\" >1240.000000</td>\n",
-       "      <td id=\"T_877ee_row25_col4\" class=\"data row25 col4\" >-0.079718</td>\n",
+       "      <th id=\"T_85bcd_level0_row25\" class=\"row_heading level0 row25\" >IH.SMS.Outbound.Loyal.pxLastOutcomeTime.DaysSince</th>\n",
+       "      <td id=\"T_85bcd_row25_col0\" class=\"data row25 col0\" >None</td>\n",
+       "      <td id=\"T_85bcd_row25_col1\" class=\"data row25 col1\" >1.000000</td>\n",
+       "      <td id=\"T_85bcd_row25_col2\" class=\"data row25 col2\" >165.000000</td>\n",
+       "      <td id=\"T_85bcd_row25_col3\" class=\"data row25 col3\" >1240.000000</td>\n",
+       "      <td id=\"T_85bcd_row25_col4\" class=\"data row25 col4\" >-0.079718</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row26\" class=\"row_heading level0 row26\" >IH.SMS.Outbound.Loyal.pyHistoricalOutcomeCount</th>\n",
-       "      <td id=\"T_877ee_row26_col0\" class=\"data row26 col0\" >None</td>\n",
-       "      <td id=\"T_877ee_row26_col1\" class=\"data row26 col1\" >1.000000</td>\n",
-       "      <td id=\"T_877ee_row26_col2\" class=\"data row26 col2\" >165.000000</td>\n",
-       "      <td id=\"T_877ee_row26_col3\" class=\"data row26 col3\" >1240.000000</td>\n",
-       "      <td id=\"T_877ee_row26_col4\" class=\"data row26 col4\" >-0.079718</td>\n",
+       "      <th id=\"T_85bcd_level0_row26\" class=\"row_heading level0 row26\" >IH.SMS.Outbound.Loyal.pyHistoricalOutcomeCount</th>\n",
+       "      <td id=\"T_85bcd_row26_col0\" class=\"data row26 col0\" >None</td>\n",
+       "      <td id=\"T_85bcd_row26_col1\" class=\"data row26 col1\" >1.000000</td>\n",
+       "      <td id=\"T_85bcd_row26_col2\" class=\"data row26 col2\" >165.000000</td>\n",
+       "      <td id=\"T_85bcd_row26_col3\" class=\"data row26 col3\" >1240.000000</td>\n",
+       "      <td id=\"T_85bcd_row26_col4\" class=\"data row26 col4\" >-0.079718</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row27\" class=\"row_heading level0 row27\" >IH.SMS.Outbound.Rejected.pxLastGroupID</th>\n",
-       "      <td id=\"T_877ee_row27_col0\" class=\"data row27 col0\" >Account</td>\n",
-       "      <td id=\"T_877ee_row27_col1\" class=\"data row27 col1\" >2.000000</td>\n",
-       "      <td id=\"T_877ee_row27_col2\" class=\"data row27 col2\" >47.000000</td>\n",
-       "      <td id=\"T_877ee_row27_col3\" class=\"data row27 col3\" >357.000000</td>\n",
-       "      <td id=\"T_877ee_row27_col4\" class=\"data row27 col4\" >-0.090492</td>\n",
+       "      <th id=\"T_85bcd_level0_row27\" class=\"row_heading level0 row27\" >IH.SMS.Outbound.Rejected.pxLastGroupID</th>\n",
+       "      <td id=\"T_85bcd_row27_col0\" class=\"data row27 col0\" >Account</td>\n",
+       "      <td id=\"T_85bcd_row27_col1\" class=\"data row27 col1\" >2.000000</td>\n",
+       "      <td id=\"T_85bcd_row27_col2\" class=\"data row27 col2\" >47.000000</td>\n",
+       "      <td id=\"T_85bcd_row27_col3\" class=\"data row27 col3\" >357.000000</td>\n",
+       "      <td id=\"T_85bcd_row27_col4\" class=\"data row27 col4\" >-0.090492</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row28\" class=\"row_heading level0 row28\" >IH.SMS.Outbound.Rejected.pyHistoricalOutcomeCount</th>\n",
-       "      <td id=\"T_877ee_row28_col0\" class=\"data row28 col0\" >102.72</td>\n",
-       "      <td id=\"T_877ee_row28_col1\" class=\"data row28 col1\" >4.000000</td>\n",
-       "      <td id=\"T_877ee_row28_col2\" class=\"data row28 col2\" >12.000000</td>\n",
-       "      <td id=\"T_877ee_row28_col3\" class=\"data row28 col3\" >117.000000</td>\n",
-       "      <td id=\"T_877ee_row28_col4\" class=\"data row28 col4\" >-0.335590</td>\n",
+       "      <th id=\"T_85bcd_level0_row28\" class=\"row_heading level0 row28\" >IH.SMS.Outbound.Rejected.pyHistoricalOutcomeCount</th>\n",
+       "      <td id=\"T_85bcd_row28_col0\" class=\"data row28 col0\" >102.72</td>\n",
+       "      <td id=\"T_85bcd_row28_col1\" class=\"data row28 col1\" >4.000000</td>\n",
+       "      <td id=\"T_85bcd_row28_col2\" class=\"data row28 col2\" >12.000000</td>\n",
+       "      <td id=\"T_85bcd_row28_col3\" class=\"data row28 col3\" >117.000000</td>\n",
+       "      <td id=\"T_85bcd_row28_col4\" class=\"data row28 col4\" >-0.335590</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row29\" class=\"row_heading level0 row29\" >IH.Web.Inbound.Accepted.pxLastGroupID</th>\n",
-       "      <td id=\"T_877ee_row29_col0\" class=\"data row29 col0\" >DepositAccounts</td>\n",
-       "      <td id=\"T_877ee_row29_col1\" class=\"data row29 col1\" >3.000000</td>\n",
-       "      <td id=\"T_877ee_row29_col2\" class=\"data row29 col2\" >53.000000</td>\n",
-       "      <td id=\"T_877ee_row29_col3\" class=\"data row29 col3\" >397.000000</td>\n",
-       "      <td id=\"T_877ee_row29_col4\" class=\"data row29 col4\" >-0.077902</td>\n",
+       "      <th id=\"T_85bcd_level0_row29\" class=\"row_heading level0 row29\" >IH.Web.Inbound.Accepted.pxLastGroupID</th>\n",
+       "      <td id=\"T_85bcd_row29_col0\" class=\"data row29 col0\" >DepositAccounts</td>\n",
+       "      <td id=\"T_85bcd_row29_col1\" class=\"data row29 col1\" >3.000000</td>\n",
+       "      <td id=\"T_85bcd_row29_col2\" class=\"data row29 col2\" >53.000000</td>\n",
+       "      <td id=\"T_85bcd_row29_col3\" class=\"data row29 col3\" >397.000000</td>\n",
+       "      <td id=\"T_85bcd_row29_col4\" class=\"data row29 col4\" >-0.077902</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row30\" class=\"row_heading level0 row30\" >IH.Web.Inbound.Accepted.pyHistoricalOutcomeCount</th>\n",
-       "      <td id=\"T_877ee_row30_col0\" class=\"data row30 col0\" >11.04</td>\n",
-       "      <td id=\"T_877ee_row30_col1\" class=\"data row30 col1\" >5.000000</td>\n",
-       "      <td id=\"T_877ee_row30_col2\" class=\"data row30 col2\" >25.000000</td>\n",
-       "      <td id=\"T_877ee_row30_col3\" class=\"data row30 col3\" >164.000000</td>\n",
-       "      <td id=\"T_877ee_row30_col4\" class=\"data row30 col4\" >0.055802</td>\n",
+       "      <th id=\"T_85bcd_level0_row30\" class=\"row_heading level0 row30\" >IH.Web.Inbound.Accepted.pyHistoricalOutcomeCount</th>\n",
+       "      <td id=\"T_85bcd_row30_col0\" class=\"data row30 col0\" >11.04</td>\n",
+       "      <td id=\"T_85bcd_row30_col1\" class=\"data row30 col1\" >5.000000</td>\n",
+       "      <td id=\"T_85bcd_row30_col2\" class=\"data row30 col2\" >25.000000</td>\n",
+       "      <td id=\"T_85bcd_row30_col3\" class=\"data row30 col3\" >164.000000</td>\n",
+       "      <td id=\"T_85bcd_row30_col4\" class=\"data row30 col4\" >0.055802</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row31\" class=\"row_heading level0 row31\" >IH.Web.Inbound.Loyal.pxLastGroupID</th>\n",
-       "      <td id=\"T_877ee_row31_col0\" class=\"data row31 col0\" >MISSING</td>\n",
-       "      <td id=\"T_877ee_row31_col1\" class=\"data row31 col1\" >1.000000</td>\n",
-       "      <td id=\"T_877ee_row31_col2\" class=\"data row31 col2\" >100.000000</td>\n",
-       "      <td id=\"T_877ee_row31_col3\" class=\"data row31 col3\" >857.000000</td>\n",
-       "      <td id=\"T_877ee_row31_col4\" class=\"data row31 col4\" >-0.211919</td>\n",
+       "      <th id=\"T_85bcd_level0_row31\" class=\"row_heading level0 row31\" >IH.Web.Inbound.Loyal.pxLastGroupID</th>\n",
+       "      <td id=\"T_85bcd_row31_col0\" class=\"data row31 col0\" >MISSING</td>\n",
+       "      <td id=\"T_85bcd_row31_col1\" class=\"data row31 col1\" >1.000000</td>\n",
+       "      <td id=\"T_85bcd_row31_col2\" class=\"data row31 col2\" >100.000000</td>\n",
+       "      <td id=\"T_85bcd_row31_col3\" class=\"data row31 col3\" >857.000000</td>\n",
+       "      <td id=\"T_85bcd_row31_col4\" class=\"data row31 col4\" >-0.211919</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row32\" class=\"row_heading level0 row32\" >IH.Web.Inbound.Loyal.pyHistoricalOutcomeCount</th>\n",
-       "      <td id=\"T_877ee_row32_col0\" class=\"data row32 col0\" >4.52</td>\n",
-       "      <td id=\"T_877ee_row32_col1\" class=\"data row32 col1\" >3.000000</td>\n",
-       "      <td id=\"T_877ee_row32_col2\" class=\"data row32 col2\" >30.000000</td>\n",
-       "      <td id=\"T_877ee_row32_col3\" class=\"data row32 col3\" >212.000000</td>\n",
-       "      <td id=\"T_877ee_row32_col4\" class=\"data row32 col4\" >-0.017224</td>\n",
+       "      <th id=\"T_85bcd_level0_row32\" class=\"row_heading level0 row32\" >IH.Web.Inbound.Loyal.pyHistoricalOutcomeCount</th>\n",
+       "      <td id=\"T_85bcd_row32_col0\" class=\"data row32 col0\" >4.52</td>\n",
+       "      <td id=\"T_85bcd_row32_col1\" class=\"data row32 col1\" >3.000000</td>\n",
+       "      <td id=\"T_85bcd_row32_col2\" class=\"data row32 col2\" >30.000000</td>\n",
+       "      <td id=\"T_85bcd_row32_col3\" class=\"data row32 col3\" >212.000000</td>\n",
+       "      <td id=\"T_85bcd_row32_col4\" class=\"data row32 col4\" >-0.017224</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row33\" class=\"row_heading level0 row33\" >IH.Web.Inbound.Rejected.pxLastGroupID</th>\n",
-       "      <td id=\"T_877ee_row33_col0\" class=\"data row33 col0\" >Account</td>\n",
-       "      <td id=\"T_877ee_row33_col1\" class=\"data row33 col1\" >2.000000</td>\n",
-       "      <td id=\"T_877ee_row33_col2\" class=\"data row33 col2\" >81.000000</td>\n",
-       "      <td id=\"T_877ee_row33_col3\" class=\"data row33 col3\" >546.000000</td>\n",
-       "      <td id=\"T_877ee_row33_col4\" class=\"data row33 col4\" >0.027864</td>\n",
+       "      <th id=\"T_85bcd_level0_row33\" class=\"row_heading level0 row33\" >IH.Web.Inbound.Rejected.pxLastGroupID</th>\n",
+       "      <td id=\"T_85bcd_row33_col0\" class=\"data row33 col0\" >Account</td>\n",
+       "      <td id=\"T_85bcd_row33_col1\" class=\"data row33 col1\" >2.000000</td>\n",
+       "      <td id=\"T_85bcd_row33_col2\" class=\"data row33 col2\" >81.000000</td>\n",
+       "      <td id=\"T_85bcd_row33_col3\" class=\"data row33 col3\" >546.000000</td>\n",
+       "      <td id=\"T_85bcd_row33_col4\" class=\"data row33 col4\" >0.027864</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row34\" class=\"row_heading level0 row34\" >IH.Web.Inbound.Rejected.pyHistoricalOutcomeCount</th>\n",
-       "      <td id=\"T_877ee_row34_col0\" class=\"data row34 col0\" >111.08</td>\n",
-       "      <td id=\"T_877ee_row34_col1\" class=\"data row34 col1\" >4.000000</td>\n",
-       "      <td id=\"T_877ee_row34_col2\" class=\"data row34 col2\" >35.000000</td>\n",
-       "      <td id=\"T_877ee_row34_col3\" class=\"data row34 col3\" >306.000000</td>\n",
-       "      <td id=\"T_877ee_row34_col4\" class=\"data row34 col4\" >-0.231670</td>\n",
+       "      <th id=\"T_85bcd_level0_row34\" class=\"row_heading level0 row34\" >IH.Web.Inbound.Rejected.pyHistoricalOutcomeCount</th>\n",
+       "      <td id=\"T_85bcd_row34_col0\" class=\"data row34 col0\" >111.08</td>\n",
+       "      <td id=\"T_85bcd_row34_col1\" class=\"data row34 col1\" >4.000000</td>\n",
+       "      <td id=\"T_85bcd_row34_col2\" class=\"data row34 col2\" >35.000000</td>\n",
+       "      <td id=\"T_85bcd_row34_col3\" class=\"data row34 col3\" >306.000000</td>\n",
+       "      <td id=\"T_85bcd_row34_col4\" class=\"data row34 col4\" >-0.231670</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row35\" class=\"row_heading level0 row35\" >Param.ExtGroupCreditcards</th>\n",
-       "      <td id=\"T_877ee_row35_col0\" class=\"data row35 col0\" >NON-MISSING</td>\n",
-       "      <td id=\"T_877ee_row35_col1\" class=\"data row35 col1\" >1.000000</td>\n",
-       "      <td id=\"T_877ee_row35_col2\" class=\"data row35 col2\" >136.000000</td>\n",
-       "      <td id=\"T_877ee_row35_col3\" class=\"data row35 col3\" >721.000000</td>\n",
-       "      <td id=\"T_877ee_row35_col4\" class=\"data row35 col4\" >0.268402</td>\n",
+       "      <th id=\"T_85bcd_level0_row35\" class=\"row_heading level0 row35\" >Param.ExtGroupCreditcards</th>\n",
+       "      <td id=\"T_85bcd_row35_col0\" class=\"data row35 col0\" >NON-MISSING</td>\n",
+       "      <td id=\"T_85bcd_row35_col1\" class=\"data row35 col1\" >1.000000</td>\n",
+       "      <td id=\"T_85bcd_row35_col2\" class=\"data row35 col2\" >136.000000</td>\n",
+       "      <td id=\"T_85bcd_row35_col3\" class=\"data row35 col3\" >721.000000</td>\n",
+       "      <td id=\"T_85bcd_row35_col4\" class=\"data row35 col4\" >0.268402</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_877ee_level0_row36\" class=\"row_heading level0 row36\" >Final Score</th>\n",
-       "      <td id=\"T_877ee_row36_col0\" class=\"data row36 col0\" >None</td>\n",
-       "      <td id=\"T_877ee_row36_col1\" class=\"data row36 col1\" >nan</td>\n",
-       "      <td id=\"T_877ee_row36_col2\" class=\"data row36 col2\" >nan</td>\n",
-       "      <td id=\"T_877ee_row36_col3\" class=\"data row36 col3\" >nan</td>\n",
-       "      <td id=\"T_877ee_row36_col4\" class=\"data row36 col4\" >-0.149329</td>\n",
+       "      <th id=\"T_85bcd_level0_row36\" class=\"row_heading level0 row36\" >Final Score</th>\n",
+       "      <td id=\"T_85bcd_row36_col0\" class=\"data row36 col0\" >None</td>\n",
+       "      <td id=\"T_85bcd_row36_col1\" class=\"data row36 col1\" >nan</td>\n",
+       "      <td id=\"T_85bcd_row36_col2\" class=\"data row36 col2\" >nan</td>\n",
+       "      <td id=\"T_85bcd_row36_col3\" class=\"data row36 col3\" >nan</td>\n",
+       "      <td id=\"T_85bcd_row36_col4\" class=\"data row36 col4\" >-0.149329</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7ff13c1ec150>"
+       "<pandas.io.formats.style.Styler at 0x7f89b0f98e90>"
       ]
      },
      "execution_count": 14,
@@ -2018,10 +2018,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:35.088023Z",
-     "iopub.status.busy": "2024-06-27T08:57:35.087691Z",
-     "iopub.status.idle": "2024-06-27T08:57:35.097044Z",
-     "shell.execute_reply": "2024-06-27T08:57:35.096602Z"
+     "iopub.execute_input": "2024-06-28T08:59:10.659369Z",
+     "iopub.status.busy": "2024-06-28T08:59:10.659036Z",
+     "iopub.status.idle": "2024-06-28T08:59:10.668144Z",
+     "shell.execute_reply": "2024-06-28T08:59:10.667666Z"
     },
     "tags": [
      "remove_input"
@@ -2032,23 +2032,23 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_348bb_row0_col5, #T_348bb_row1_col5, #T_348bb_row2_col5, #T_348bb_row3_col5, #T_348bb_row4_col5, #T_348bb_row5_col5, #T_348bb_row6_col5, #T_348bb_row7_col5, #T_348bb_row8_col5, #T_348bb_row9_col5, #T_348bb_row10_col5, #T_348bb_row11_col5, #T_348bb_row12_col5, #T_348bb_row13_col5, #T_348bb_row14_col5 {\n",
+       "#T_11d2d_row0_col5, #T_11d2d_row1_col5, #T_11d2d_row2_col5, #T_11d2d_row3_col5, #T_11d2d_row4_col5, #T_11d2d_row5_col5, #T_11d2d_row6_col5, #T_11d2d_row7_col5, #T_11d2d_row8_col5, #T_11d2d_row9_col5, #T_11d2d_row10_col5, #T_11d2d_row11_col5, #T_11d2d_row12_col5, #T_11d2d_row13_col5, #T_11d2d_row14_col5 {\n",
        "  color: blue;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_348bb\">\n",
+       "<table id=\"T_11d2d\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_348bb_level0_col0\" class=\"col_heading level0 col0\" >Bin</th>\n",
-       "      <th id=\"T_348bb_level0_col1\" class=\"col_heading level0 col1\" >Positives</th>\n",
-       "      <th id=\"T_348bb_level0_col2\" class=\"col_heading level0 col2\" >Negatives</th>\n",
-       "      <th id=\"T_348bb_level0_col3\" class=\"col_heading level0 col3\" >Cum. Total (%)</th>\n",
-       "      <th id=\"T_348bb_level0_col4\" class=\"col_heading level0 col4\" >Propensity (%)</th>\n",
-       "      <th id=\"T_348bb_level0_col5\" class=\"col_heading level0 col5\" >Adjusted Propensity (%)</th>\n",
-       "      <th id=\"T_348bb_level0_col6\" class=\"col_heading level0 col6\" >Cum Positives (%)</th>\n",
-       "      <th id=\"T_348bb_level0_col7\" class=\"col_heading level0 col7\" >ZRatio</th>\n",
-       "      <th id=\"T_348bb_level0_col8\" class=\"col_heading level0 col8\" >Lift(%)</th>\n",
+       "      <th id=\"T_11d2d_level0_col0\" class=\"col_heading level0 col0\" >Bin</th>\n",
+       "      <th id=\"T_11d2d_level0_col1\" class=\"col_heading level0 col1\" >Positives</th>\n",
+       "      <th id=\"T_11d2d_level0_col2\" class=\"col_heading level0 col2\" >Negatives</th>\n",
+       "      <th id=\"T_11d2d_level0_col3\" class=\"col_heading level0 col3\" >Cum. Total (%)</th>\n",
+       "      <th id=\"T_11d2d_level0_col4\" class=\"col_heading level0 col4\" >Propensity (%)</th>\n",
+       "      <th id=\"T_11d2d_level0_col5\" class=\"col_heading level0 col5\" >Adjusted Propensity (%)</th>\n",
+       "      <th id=\"T_11d2d_level0_col6\" class=\"col_heading level0 col6\" >Cum Positives (%)</th>\n",
+       "      <th id=\"T_11d2d_level0_col7\" class=\"col_heading level0 col7\" >ZRatio</th>\n",
+       "      <th id=\"T_11d2d_level0_col8\" class=\"col_heading level0 col8\" >Lift(%)</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th class=\"index_name level0\" >Index</th>\n",
@@ -2065,190 +2065,190 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_348bb_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_348bb_row0_col0\" class=\"data row0 col0\" ><-0.21</td>\n",
-       "      <td id=\"T_348bb_row0_col1\" class=\"data row0 col1\" >17</td>\n",
-       "      <td id=\"T_348bb_row0_col2\" class=\"data row0 col2\" >443</td>\n",
-       "      <td id=\"T_348bb_row0_col3\" class=\"data row0 col3\" >28.117361</td>\n",
-       "      <td id=\"T_348bb_row0_col4\" class=\"data row0 col4\" >3.695652</td>\n",
-       "      <td id=\"T_348bb_row0_col5\" class=\"data row0 col5\" >3.796095</td>\n",
-       "      <td id=\"T_348bb_row0_col6\" class=\"data row0 col6\" >8.252427</td>\n",
-       "      <td id=\"T_348bb_row0_col7\" class=\"data row0 col7\" >-9.994484</td>\n",
-       "      <td id=\"T_348bb_row0_col8\" class=\"data row0 col8\" >1.956662</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_348bb_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_348bb_row1_col0\" class=\"data row1 col0\" >[-0.21, -0.185></td>\n",
-       "      <td id=\"T_348bb_row1_col1\" class=\"data row1 col1\" >8</td>\n",
-       "      <td id=\"T_348bb_row1_col2\" class=\"data row1 col2\" >133</td>\n",
-       "      <td id=\"T_348bb_row1_col3\" class=\"data row1 col3\" >36.735943</td>\n",
-       "      <td id=\"T_348bb_row1_col4\" class=\"data row1 col4\" >5.673759</td>\n",
-       "      <td id=\"T_348bb_row1_col5\" class=\"data row1 col5\" >5.985916</td>\n",
-       "      <td id=\"T_348bb_row1_col6\" class=\"data row1 col6\" >12.135922</td>\n",
-       "      <td id=\"T_348bb_row1_col7\" class=\"data row1 col7\" >-3.495416</td>\n",
-       "      <td id=\"T_348bb_row1_col8\" class=\"data row1 col8\" >3.003971</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_348bb_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_348bb_row2_col0\" class=\"data row2 col0\" >[-0.185, -0.175></td>\n",
-       "      <td id=\"T_348bb_row2_col1\" class=\"data row2 col1\" >3</td>\n",
-       "      <td id=\"T_348bb_row2_col2\" class=\"data row2 col2\" >48</td>\n",
-       "      <td id=\"T_348bb_row2_col3\" class=\"data row2 col3\" >39.853302</td>\n",
-       "      <td id=\"T_348bb_row2_col4\" class=\"data row2 col4\" >5.882353</td>\n",
-       "      <td id=\"T_348bb_row2_col5\" class=\"data row2 col5\" >6.730770</td>\n",
-       "      <td id=\"T_348bb_row2_col6\" class=\"data row2 col6\" >13.592233</td>\n",
-       "      <td id=\"T_348bb_row2_col7\" class=\"data row2 col7\" >-1.977473</td>\n",
-       "      <td id=\"T_348bb_row2_col8\" class=\"data row2 col8\" >3.114411</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_348bb_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_348bb_row3_col0\" class=\"data row3 col0\" >[-0.175, -0.105></td>\n",
-       "      <td id=\"T_348bb_row3_col1\" class=\"data row3 col1\" >28</td>\n",
-       "      <td id=\"T_348bb_row3_col2\" class=\"data row3 col2\" >370</td>\n",
-       "      <td id=\"T_348bb_row3_col3\" class=\"data row3 col3\" >64.180931</td>\n",
-       "      <td id=\"T_348bb_row3_col4\" class=\"data row3 col4\" >7.035176</td>\n",
-       "      <td id=\"T_348bb_row3_col5\" class=\"data row3 col5\" >7.142858</td>\n",
-       "      <td id=\"T_348bb_row3_col6\" class=\"data row3 col6\" >27.184465</td>\n",
-       "      <td id=\"T_348bb_row3_col7\" class=\"data row3 col7\" >-4.628075</td>\n",
-       "      <td id=\"T_348bb_row3_col8\" class=\"data row3 col8\" >3.724772</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_348bb_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "      <td id=\"T_348bb_row4_col0\" class=\"data row4 col0\" >[-0.105, -0.095></td>\n",
-       "      <td id=\"T_348bb_row4_col1\" class=\"data row4 col1\" >4</td>\n",
-       "      <td id=\"T_348bb_row4_col2\" class=\"data row4 col2\" >51</td>\n",
-       "      <td id=\"T_348bb_row4_col3\" class=\"data row4 col3\" >67.542793</td>\n",
-       "      <td id=\"T_348bb_row4_col4\" class=\"data row4 col4\" >7.272727</td>\n",
-       "      <td id=\"T_348bb_row4_col5\" class=\"data row4 col5\" >8.035714</td>\n",
-       "      <td id=\"T_348bb_row4_col6\" class=\"data row4 col6\" >29.126215</td>\n",
-       "      <td id=\"T_348bb_row4_col7\" class=\"data row4 col7\" >-1.505372</td>\n",
-       "      <td id=\"T_348bb_row4_col8\" class=\"data row4 col8\" >3.850544</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_348bb_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
-       "      <td id=\"T_348bb_row5_col0\" class=\"data row5 col0\" >[-0.095, -0.09></td>\n",
-       "      <td id=\"T_348bb_row5_col1\" class=\"data row5 col1\" >2</td>\n",
-       "      <td id=\"T_348bb_row5_col2\" class=\"data row5 col2\" >19</td>\n",
-       "      <td id=\"T_348bb_row5_col3\" class=\"data row5 col3\" >68.826408</td>\n",
-       "      <td id=\"T_348bb_row5_col4\" class=\"data row5 col4\" >9.523809</td>\n",
-       "      <td id=\"T_348bb_row5_col5\" class=\"data row5 col5\" >11.363637</td>\n",
-       "      <td id=\"T_348bb_row5_col6\" class=\"data row5 col6\" >30.097088</td>\n",
-       "      <td id=\"T_348bb_row5_col7\" class=\"data row5 col7\" >-0.478811</td>\n",
-       "      <td id=\"T_348bb_row5_col8\" class=\"data row5 col8\" >5.042379</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_348bb_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
-       "      <td id=\"T_348bb_row6_col0\" class=\"data row6 col0\" >[-0.09, -0.065></td>\n",
-       "      <td id=\"T_348bb_row6_col1\" class=\"data row6 col1\" >9</td>\n",
-       "      <td id=\"T_348bb_row6_col2\" class=\"data row6 col2\" >77</td>\n",
-       "      <td id=\"T_348bb_row6_col3\" class=\"data row6 col3\" >74.083130</td>\n",
-       "      <td id=\"T_348bb_row6_col4\" class=\"data row6 col4\" >10.465117</td>\n",
-       "      <td id=\"T_348bb_row6_col5\" class=\"data row6 col5\" >10.919540</td>\n",
-       "      <td id=\"T_348bb_row6_col6\" class=\"data row6 col6\" >34.466019</td>\n",
-       "      <td id=\"T_348bb_row6_col7\" class=\"data row6 col7\" >-0.657755</td>\n",
-       "      <td id=\"T_348bb_row6_col8\" class=\"data row6 col8\" >5.540754</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_348bb_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
-       "      <td id=\"T_348bb_row7_col0\" class=\"data row7 col0\" >[-0.065, -0.02></td>\n",
-       "      <td id=\"T_348bb_row7_col1\" class=\"data row7 col1\" >30</td>\n",
-       "      <td id=\"T_348bb_row7_col2\" class=\"data row7 col2\" >154</td>\n",
-       "      <td id=\"T_348bb_row7_col3\" class=\"data row7 col3\" >85.330078</td>\n",
-       "      <td id=\"T_348bb_row7_col4\" class=\"data row7 col4\" >16.304348</td>\n",
-       "      <td id=\"T_348bb_row7_col5\" class=\"data row7 col5\" >16.486486</td>\n",
-       "      <td id=\"T_348bb_row7_col6\" class=\"data row7 col6\" >49.029125</td>\n",
-       "      <td id=\"T_348bb_row7_col7\" class=\"data row7 col7\" >1.464400</td>\n",
-       "      <td id=\"T_348bb_row7_col8\" class=\"data row7 col8\" >8.632335</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_348bb_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
-       "      <td id=\"T_348bb_row8_col0\" class=\"data row8 col0\" >[-0.02, 0.03></td>\n",
-       "      <td id=\"T_348bb_row8_col1\" class=\"data row8 col1\" >37</td>\n",
-       "      <td id=\"T_348bb_row8_col2\" class=\"data row8 col2\" >65</td>\n",
-       "      <td id=\"T_348bb_row8_col3\" class=\"data row8 col3\" >91.564796</td>\n",
-       "      <td id=\"T_348bb_row8_col4\" class=\"data row8 col4\" >36.274509</td>\n",
-       "      <td id=\"T_348bb_row8_col5\" class=\"data row8 col5\" >36.407764</td>\n",
-       "      <td id=\"T_348bb_row8_col6\" class=\"data row8 col6\" >66.990295</td>\n",
-       "      <td id=\"T_348bb_row8_col7\" class=\"data row8 col7\" >4.913029</td>\n",
-       "      <td id=\"T_348bb_row8_col8\" class=\"data row8 col8\" >19.205532</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_348bb_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
-       "      <td id=\"T_348bb_row9_col0\" class=\"data row9 col0\" >[0.03, 0.06></td>\n",
-       "      <td id=\"T_348bb_row9_col1\" class=\"data row9 col1\" >20</td>\n",
-       "      <td id=\"T_348bb_row9_col2\" class=\"data row9 col2\" >29</td>\n",
-       "      <td id=\"T_348bb_row9_col3\" class=\"data row9 col3\" >94.559906</td>\n",
-       "      <td id=\"T_348bb_row9_col4\" class=\"data row9 col4\" >40.816326</td>\n",
-       "      <td id=\"T_348bb_row9_col5\" class=\"data row9 col5\" >41.000000</td>\n",
-       "      <td id=\"T_348bb_row9_col6\" class=\"data row9 col6\" >76.699028</td>\n",
-       "      <td id=\"T_348bb_row9_col7\" class=\"data row9 col7\" >3.664015</td>\n",
-       "      <td id=\"T_348bb_row9_col8\" class=\"data row9 col8\" >21.610197</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_348bb_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
-       "      <td id=\"T_348bb_row10_col0\" class=\"data row10 col0\" >[0.06, 0.12></td>\n",
-       "      <td id=\"T_348bb_row10_col1\" class=\"data row10 col1\" >30</td>\n",
-       "      <td id=\"T_348bb_row10_col2\" class=\"data row10 col2\" >33</td>\n",
-       "      <td id=\"T_348bb_row10_col3\" class=\"data row10 col3\" >98.410759</td>\n",
-       "      <td id=\"T_348bb_row10_col4\" class=\"data row10 col4\" >47.619049</td>\n",
-       "      <td id=\"T_348bb_row10_col5\" class=\"data row10 col5\" >47.656250</td>\n",
-       "      <td id=\"T_348bb_row10_col6\" class=\"data row10 col6\" >91.262138</td>\n",
-       "      <td id=\"T_348bb_row10_col7\" class=\"data row10 col7\" >4.922851</td>\n",
-       "      <td id=\"T_348bb_row10_col8\" class=\"data row10 col8\" >25.211897</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_348bb_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
-       "      <td id=\"T_348bb_row11_col0\" class=\"data row11 col0\" >[0.12, 0.125></td>\n",
-       "      <td id=\"T_348bb_row11_col1\" class=\"data row11 col1\" >2</td>\n",
-       "      <td id=\"T_348bb_row11_col2\" class=\"data row11 col2\" >2</td>\n",
-       "      <td id=\"T_348bb_row11_col3\" class=\"data row11 col3\" >98.655258</td>\n",
-       "      <td id=\"T_348bb_row11_col4\" class=\"data row11 col4\" >50.000000</td>\n",
-       "      <td id=\"T_348bb_row11_col5\" class=\"data row11 col5\" >50.000000</td>\n",
-       "      <td id=\"T_348bb_row11_col6\" class=\"data row11 col6\" >92.233009</td>\n",
-       "      <td id=\"T_348bb_row11_col7\" class=\"data row11 col7\" >1.203876</td>\n",
-       "      <td id=\"T_348bb_row11_col8\" class=\"data row11 col8\" >26.472490</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_348bb_level0_row12\" class=\"row_heading level0 row12\" >13</th>\n",
-       "      <td id=\"T_348bb_row12_col0\" class=\"data row12 col0\" >[0.125, 0.13></td>\n",
-       "      <td id=\"T_348bb_row12_col1\" class=\"data row12 col1\" >4</td>\n",
-       "      <td id=\"T_348bb_row12_col2\" class=\"data row12 col2\" >2</td>\n",
-       "      <td id=\"T_348bb_row12_col3\" class=\"data row12 col3\" >99.022003</td>\n",
-       "      <td id=\"T_348bb_row12_col4\" class=\"data row12 col4\" >66.666672</td>\n",
-       "      <td id=\"T_348bb_row12_col5\" class=\"data row12 col5\" >64.285713</td>\n",
-       "      <td id=\"T_348bb_row12_col6\" class=\"data row12 col6\" >94.174759</td>\n",
-       "      <td id=\"T_348bb_row12_col7\" class=\"data row12 col7\" >1.864405</td>\n",
-       "      <td id=\"T_348bb_row12_col8\" class=\"data row12 col8\" >35.296654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_348bb_level0_row13\" class=\"row_heading level0 row13\" >14</th>\n",
-       "      <td id=\"T_348bb_row13_col0\" class=\"data row13 col0\" >[0.13, 0.995></td>\n",
-       "      <td id=\"T_348bb_row13_col1\" class=\"data row13 col1\" >8</td>\n",
-       "      <td id=\"T_348bb_row13_col2\" class=\"data row13 col2\" >3</td>\n",
-       "      <td id=\"T_348bb_row13_col3\" class=\"data row13 col3\" >99.694382</td>\n",
-       "      <td id=\"T_348bb_row13_col4\" class=\"data row13 col4\" >72.727272</td>\n",
-       "      <td id=\"T_348bb_row13_col5\" class=\"data row13 col5\" >70.833328</td>\n",
-       "      <td id=\"T_348bb_row13_col6\" class=\"data row13 col6\" >98.058250</td>\n",
-       "      <td id=\"T_348bb_row13_col7\" class=\"data row13 col7\" >2.718192</td>\n",
-       "      <td id=\"T_348bb_row13_col8\" class=\"data row13 col8\" >38.505444</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_348bb_level0_row14\" class=\"row_heading level0 row14\" >15</th>\n",
-       "      <td id=\"T_348bb_row14_col0\" class=\"data row14 col0\" >>=0.995</td>\n",
-       "      <td id=\"T_348bb_row14_col1\" class=\"data row14 col1\" >4</td>\n",
-       "      <td id=\"T_348bb_row14_col2\" class=\"data row14 col2\" >1</td>\n",
-       "      <td id=\"T_348bb_row14_col3\" class=\"data row14 col3\" >100.000000</td>\n",
-       "      <td id=\"T_348bb_row14_col4\" class=\"data row14 col4\" >80.000000</td>\n",
-       "      <td id=\"T_348bb_row14_col5\" class=\"data row14 col5\" >75.000000</td>\n",
-       "      <td id=\"T_348bb_row14_col6\" class=\"data row14 col6\" >100.000000</td>\n",
-       "      <td id=\"T_348bb_row14_col7\" class=\"data row14 col7\" >1.941841</td>\n",
-       "      <td id=\"T_348bb_row14_col8\" class=\"data row14 col8\" >42.355988</td>\n",
+       "      <th id=\"T_11d2d_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_11d2d_row0_col0\" class=\"data row0 col0\" ><-0.21</td>\n",
+       "      <td id=\"T_11d2d_row0_col1\" class=\"data row0 col1\" >17</td>\n",
+       "      <td id=\"T_11d2d_row0_col2\" class=\"data row0 col2\" >443</td>\n",
+       "      <td id=\"T_11d2d_row0_col3\" class=\"data row0 col3\" >28.117361</td>\n",
+       "      <td id=\"T_11d2d_row0_col4\" class=\"data row0 col4\" >3.695652</td>\n",
+       "      <td id=\"T_11d2d_row0_col5\" class=\"data row0 col5\" >3.796095</td>\n",
+       "      <td id=\"T_11d2d_row0_col6\" class=\"data row0 col6\" >8.252427</td>\n",
+       "      <td id=\"T_11d2d_row0_col7\" class=\"data row0 col7\" >-9.994484</td>\n",
+       "      <td id=\"T_11d2d_row0_col8\" class=\"data row0 col8\" >1.956662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_11d2d_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
+       "      <td id=\"T_11d2d_row1_col0\" class=\"data row1 col0\" >[-0.21, -0.185></td>\n",
+       "      <td id=\"T_11d2d_row1_col1\" class=\"data row1 col1\" >8</td>\n",
+       "      <td id=\"T_11d2d_row1_col2\" class=\"data row1 col2\" >133</td>\n",
+       "      <td id=\"T_11d2d_row1_col3\" class=\"data row1 col3\" >36.735943</td>\n",
+       "      <td id=\"T_11d2d_row1_col4\" class=\"data row1 col4\" >5.673759</td>\n",
+       "      <td id=\"T_11d2d_row1_col5\" class=\"data row1 col5\" >5.985916</td>\n",
+       "      <td id=\"T_11d2d_row1_col6\" class=\"data row1 col6\" >12.135922</td>\n",
+       "      <td id=\"T_11d2d_row1_col7\" class=\"data row1 col7\" >-3.495416</td>\n",
+       "      <td id=\"T_11d2d_row1_col8\" class=\"data row1 col8\" >3.003971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_11d2d_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
+       "      <td id=\"T_11d2d_row2_col0\" class=\"data row2 col0\" >[-0.185, -0.175></td>\n",
+       "      <td id=\"T_11d2d_row2_col1\" class=\"data row2 col1\" >3</td>\n",
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+       "      <td id=\"T_11d2d_row2_col3\" class=\"data row2 col3\" >39.853302</td>\n",
+       "      <td id=\"T_11d2d_row2_col4\" class=\"data row2 col4\" >5.882353</td>\n",
+       "      <td id=\"T_11d2d_row2_col5\" class=\"data row2 col5\" >6.730770</td>\n",
+       "      <td id=\"T_11d2d_row2_col6\" class=\"data row2 col6\" >13.592233</td>\n",
+       "      <td id=\"T_11d2d_row2_col7\" class=\"data row2 col7\" >-1.977473</td>\n",
+       "      <td id=\"T_11d2d_row2_col8\" class=\"data row2 col8\" >3.114411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_11d2d_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
+       "      <td id=\"T_11d2d_row3_col0\" class=\"data row3 col0\" >[-0.175, -0.105></td>\n",
+       "      <td id=\"T_11d2d_row3_col1\" class=\"data row3 col1\" >28</td>\n",
+       "      <td id=\"T_11d2d_row3_col2\" class=\"data row3 col2\" >370</td>\n",
+       "      <td id=\"T_11d2d_row3_col3\" class=\"data row3 col3\" >64.180931</td>\n",
+       "      <td id=\"T_11d2d_row3_col4\" class=\"data row3 col4\" >7.035176</td>\n",
+       "      <td id=\"T_11d2d_row3_col5\" class=\"data row3 col5\" >7.142858</td>\n",
+       "      <td id=\"T_11d2d_row3_col6\" class=\"data row3 col6\" >27.184465</td>\n",
+       "      <td id=\"T_11d2d_row3_col7\" class=\"data row3 col7\" >-4.628075</td>\n",
+       "      <td id=\"T_11d2d_row3_col8\" class=\"data row3 col8\" >3.724772</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_11d2d_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
+       "      <td id=\"T_11d2d_row4_col0\" class=\"data row4 col0\" >[-0.105, -0.095></td>\n",
+       "      <td id=\"T_11d2d_row4_col1\" class=\"data row4 col1\" >4</td>\n",
+       "      <td id=\"T_11d2d_row4_col2\" class=\"data row4 col2\" >51</td>\n",
+       "      <td id=\"T_11d2d_row4_col3\" class=\"data row4 col3\" >67.542793</td>\n",
+       "      <td id=\"T_11d2d_row4_col4\" class=\"data row4 col4\" >7.272727</td>\n",
+       "      <td id=\"T_11d2d_row4_col5\" class=\"data row4 col5\" >8.035714</td>\n",
+       "      <td id=\"T_11d2d_row4_col6\" class=\"data row4 col6\" >29.126215</td>\n",
+       "      <td id=\"T_11d2d_row4_col7\" class=\"data row4 col7\" >-1.505372</td>\n",
+       "      <td id=\"T_11d2d_row4_col8\" class=\"data row4 col8\" >3.850544</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_11d2d_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
+       "      <td id=\"T_11d2d_row5_col0\" class=\"data row5 col0\" >[-0.095, -0.09></td>\n",
+       "      <td id=\"T_11d2d_row5_col1\" class=\"data row5 col1\" >2</td>\n",
+       "      <td id=\"T_11d2d_row5_col2\" class=\"data row5 col2\" >19</td>\n",
+       "      <td id=\"T_11d2d_row5_col3\" class=\"data row5 col3\" >68.826408</td>\n",
+       "      <td id=\"T_11d2d_row5_col4\" class=\"data row5 col4\" >9.523809</td>\n",
+       "      <td id=\"T_11d2d_row5_col5\" class=\"data row5 col5\" >11.363637</td>\n",
+       "      <td id=\"T_11d2d_row5_col6\" class=\"data row5 col6\" >30.097088</td>\n",
+       "      <td id=\"T_11d2d_row5_col7\" class=\"data row5 col7\" >-0.478811</td>\n",
+       "      <td id=\"T_11d2d_row5_col8\" class=\"data row5 col8\" >5.042379</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_11d2d_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
+       "      <td id=\"T_11d2d_row6_col0\" class=\"data row6 col0\" >[-0.09, -0.065></td>\n",
+       "      <td id=\"T_11d2d_row6_col1\" class=\"data row6 col1\" >9</td>\n",
+       "      <td id=\"T_11d2d_row6_col2\" class=\"data row6 col2\" >77</td>\n",
+       "      <td id=\"T_11d2d_row6_col3\" class=\"data row6 col3\" >74.083130</td>\n",
+       "      <td id=\"T_11d2d_row6_col4\" class=\"data row6 col4\" >10.465117</td>\n",
+       "      <td id=\"T_11d2d_row6_col5\" class=\"data row6 col5\" >10.919540</td>\n",
+       "      <td id=\"T_11d2d_row6_col6\" class=\"data row6 col6\" >34.466019</td>\n",
+       "      <td id=\"T_11d2d_row6_col7\" class=\"data row6 col7\" >-0.657755</td>\n",
+       "      <td id=\"T_11d2d_row6_col8\" class=\"data row6 col8\" >5.540754</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_11d2d_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
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+       "      <td id=\"T_11d2d_row7_col1\" class=\"data row7 col1\" >30</td>\n",
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+       "      <td id=\"T_11d2d_row7_col3\" class=\"data row7 col3\" >85.330078</td>\n",
+       "      <td id=\"T_11d2d_row7_col4\" class=\"data row7 col4\" >16.304348</td>\n",
+       "      <td id=\"T_11d2d_row7_col5\" class=\"data row7 col5\" >16.486486</td>\n",
+       "      <td id=\"T_11d2d_row7_col6\" class=\"data row7 col6\" >49.029125</td>\n",
+       "      <td id=\"T_11d2d_row7_col7\" class=\"data row7 col7\" >1.464400</td>\n",
+       "      <td id=\"T_11d2d_row7_col8\" class=\"data row7 col8\" >8.632335</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_11d2d_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
+       "      <td id=\"T_11d2d_row8_col0\" class=\"data row8 col0\" >[-0.02, 0.03></td>\n",
+       "      <td id=\"T_11d2d_row8_col1\" class=\"data row8 col1\" >37</td>\n",
+       "      <td id=\"T_11d2d_row8_col2\" class=\"data row8 col2\" >65</td>\n",
+       "      <td id=\"T_11d2d_row8_col3\" class=\"data row8 col3\" >91.564796</td>\n",
+       "      <td id=\"T_11d2d_row8_col4\" class=\"data row8 col4\" >36.274509</td>\n",
+       "      <td id=\"T_11d2d_row8_col5\" class=\"data row8 col5\" >36.407764</td>\n",
+       "      <td id=\"T_11d2d_row8_col6\" class=\"data row8 col6\" >66.990295</td>\n",
+       "      <td id=\"T_11d2d_row8_col7\" class=\"data row8 col7\" >4.913029</td>\n",
+       "      <td id=\"T_11d2d_row8_col8\" class=\"data row8 col8\" >19.205532</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_11d2d_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
+       "      <td id=\"T_11d2d_row9_col0\" class=\"data row9 col0\" >[0.03, 0.06></td>\n",
+       "      <td id=\"T_11d2d_row9_col1\" class=\"data row9 col1\" >20</td>\n",
+       "      <td id=\"T_11d2d_row9_col2\" class=\"data row9 col2\" >29</td>\n",
+       "      <td id=\"T_11d2d_row9_col3\" class=\"data row9 col3\" >94.559906</td>\n",
+       "      <td id=\"T_11d2d_row9_col4\" class=\"data row9 col4\" >40.816326</td>\n",
+       "      <td id=\"T_11d2d_row9_col5\" class=\"data row9 col5\" >41.000000</td>\n",
+       "      <td id=\"T_11d2d_row9_col6\" class=\"data row9 col6\" >76.699028</td>\n",
+       "      <td id=\"T_11d2d_row9_col7\" class=\"data row9 col7\" >3.664015</td>\n",
+       "      <td id=\"T_11d2d_row9_col8\" class=\"data row9 col8\" >21.610197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_11d2d_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
+       "      <td id=\"T_11d2d_row10_col0\" class=\"data row10 col0\" >[0.06, 0.12></td>\n",
+       "      <td id=\"T_11d2d_row10_col1\" class=\"data row10 col1\" >30</td>\n",
+       "      <td id=\"T_11d2d_row10_col2\" class=\"data row10 col2\" >33</td>\n",
+       "      <td id=\"T_11d2d_row10_col3\" class=\"data row10 col3\" >98.410759</td>\n",
+       "      <td id=\"T_11d2d_row10_col4\" class=\"data row10 col4\" >47.619049</td>\n",
+       "      <td id=\"T_11d2d_row10_col5\" class=\"data row10 col5\" >47.656250</td>\n",
+       "      <td id=\"T_11d2d_row10_col6\" class=\"data row10 col6\" >91.262138</td>\n",
+       "      <td id=\"T_11d2d_row10_col7\" class=\"data row10 col7\" >4.922851</td>\n",
+       "      <td id=\"T_11d2d_row10_col8\" class=\"data row10 col8\" >25.211897</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_11d2d_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
+       "      <td id=\"T_11d2d_row11_col0\" class=\"data row11 col0\" >[0.12, 0.125></td>\n",
+       "      <td id=\"T_11d2d_row11_col1\" class=\"data row11 col1\" >2</td>\n",
+       "      <td id=\"T_11d2d_row11_col2\" class=\"data row11 col2\" >2</td>\n",
+       "      <td id=\"T_11d2d_row11_col3\" class=\"data row11 col3\" >98.655258</td>\n",
+       "      <td id=\"T_11d2d_row11_col4\" class=\"data row11 col4\" >50.000000</td>\n",
+       "      <td id=\"T_11d2d_row11_col5\" class=\"data row11 col5\" >50.000000</td>\n",
+       "      <td id=\"T_11d2d_row11_col6\" class=\"data row11 col6\" >92.233009</td>\n",
+       "      <td id=\"T_11d2d_row11_col7\" class=\"data row11 col7\" >1.203876</td>\n",
+       "      <td id=\"T_11d2d_row11_col8\" class=\"data row11 col8\" >26.472490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_11d2d_level0_row12\" class=\"row_heading level0 row12\" >13</th>\n",
+       "      <td id=\"T_11d2d_row12_col0\" class=\"data row12 col0\" >[0.125, 0.13></td>\n",
+       "      <td id=\"T_11d2d_row12_col1\" class=\"data row12 col1\" >4</td>\n",
+       "      <td id=\"T_11d2d_row12_col2\" class=\"data row12 col2\" >2</td>\n",
+       "      <td id=\"T_11d2d_row12_col3\" class=\"data row12 col3\" >99.022003</td>\n",
+       "      <td id=\"T_11d2d_row12_col4\" class=\"data row12 col4\" >66.666672</td>\n",
+       "      <td id=\"T_11d2d_row12_col5\" class=\"data row12 col5\" >64.285713</td>\n",
+       "      <td id=\"T_11d2d_row12_col6\" class=\"data row12 col6\" >94.174759</td>\n",
+       "      <td id=\"T_11d2d_row12_col7\" class=\"data row12 col7\" >1.864405</td>\n",
+       "      <td id=\"T_11d2d_row12_col8\" class=\"data row12 col8\" >35.296654</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_11d2d_level0_row13\" class=\"row_heading level0 row13\" >14</th>\n",
+       "      <td id=\"T_11d2d_row13_col0\" class=\"data row13 col0\" >[0.13, 0.995></td>\n",
+       "      <td id=\"T_11d2d_row13_col1\" class=\"data row13 col1\" >8</td>\n",
+       "      <td id=\"T_11d2d_row13_col2\" class=\"data row13 col2\" >3</td>\n",
+       "      <td id=\"T_11d2d_row13_col3\" class=\"data row13 col3\" >99.694382</td>\n",
+       "      <td id=\"T_11d2d_row13_col4\" class=\"data row13 col4\" >72.727272</td>\n",
+       "      <td id=\"T_11d2d_row13_col5\" class=\"data row13 col5\" >70.833328</td>\n",
+       "      <td id=\"T_11d2d_row13_col6\" class=\"data row13 col6\" >98.058250</td>\n",
+       "      <td id=\"T_11d2d_row13_col7\" class=\"data row13 col7\" >2.718192</td>\n",
+       "      <td id=\"T_11d2d_row13_col8\" class=\"data row13 col8\" >38.505444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_11d2d_level0_row14\" class=\"row_heading level0 row14\" >15</th>\n",
+       "      <td id=\"T_11d2d_row14_col0\" class=\"data row14 col0\" >>=0.995</td>\n",
+       "      <td id=\"T_11d2d_row14_col1\" class=\"data row14 col1\" >4</td>\n",
+       "      <td id=\"T_11d2d_row14_col2\" class=\"data row14 col2\" >1</td>\n",
+       "      <td id=\"T_11d2d_row14_col3\" class=\"data row14 col3\" >100.000000</td>\n",
+       "      <td id=\"T_11d2d_row14_col4\" class=\"data row14 col4\" >80.000000</td>\n",
+       "      <td id=\"T_11d2d_row14_col5\" class=\"data row14 col5\" >75.000000</td>\n",
+       "      <td id=\"T_11d2d_row14_col6\" class=\"data row14 col6\" >100.000000</td>\n",
+       "      <td id=\"T_11d2d_row14_col7\" class=\"data row14 col7\" >1.941841</td>\n",
+       "      <td id=\"T_11d2d_row14_col8\" class=\"data row14 col8\" >42.355988</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7ff13cfec310>"
+       "<pandas.io.formats.style.Styler at 0x7f8986e996d0>"
       ]
      },
      "execution_count": 15,
@@ -2285,10 +2285,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:35.099042Z",
-     "iopub.status.busy": "2024-06-27T08:57:35.098710Z",
-     "iopub.status.idle": "2024-06-27T08:57:35.131121Z",
-     "shell.execute_reply": "2024-06-27T08:57:35.130674Z"
+     "iopub.execute_input": "2024-06-28T08:59:10.670159Z",
+     "iopub.status.busy": "2024-06-28T08:59:10.669833Z",
+     "iopub.status.idle": "2024-06-28T08:59:10.702175Z",
+     "shell.execute_reply": "2024-06-28T08:59:10.701744Z"
     },
     "tags": [
      "remove_input"
@@ -2299,11 +2299,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_2994/2151495297.py:3: DeprecationWarning:\n",
+      "/tmp/ipykernel_2930/2151495297.py:3: DeprecationWarning:\n",
       "\n",
       "`where` is deprecated. Use `filter` instead.\n",
       "\n",
-      "/tmp/ipykernel_2994/2151495297.py:25: DeprecationWarning:\n",
+      "/tmp/ipykernel_2930/2151495297.py:25: DeprecationWarning:\n",
       "\n",
       "`where` is deprecated. Use `filter` instead.\n",
       "\n"
@@ -2312,9 +2312,9 @@
     {
      "data": {
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diff --git a/Python/articles/AGBModelVisualisation.html b/Python/articles/AGBModelVisualisation.html
index 09a2f8af..0662a013 100644
--- a/Python/articles/AGBModelVisualisation.html
+++ b/Python/articles/AGBModelVisualisation.html
@@ -716,7 +716,7 @@ <h2>Exploring the ADMTrees class<a class="headerlink" href="#Exploring-the-ADMTr
   white-space: pre-wrap;
 }
 </style>
-<small>shape: (5, 6)</small><table border="1" class="dataframe"><thead><tr><th>treeID</th><th>score</th><th>depth</th><th>nsplits</th><th>gains</th><th>meangains</th></tr><tr><td>i64</td><td>f64</td><td>i64</td><td>i64</td><td>list[f64]</td><td>f64</td></tr></thead><tbody><tr><td>4</td><td>-0.294892</td><td>9</td><td>25</td><td>[295.811814, 362.750664, … 4.187443]</td><td>30.616696</td></tr><tr><td>3</td><td>-0.32778</td><td>8</td><td>22</td><td>[210.495522, 3.745353, … 7.378691]</td><td>34.085356</td></tr><tr><td>49</td><td>-0.02314</td><td>8</td><td>31</td><td>[1.770591, 2.206005, … 4.969639]</td><td>2.431512</td></tr><tr><td>23</td><td>-0.025396</td><td>7</td><td>29</td><td>[15.175539, 0.007353, … 2.172846]</td><td>2.854175</td></tr><tr><td>33</td><td>-0.020448</td><td>9</td><td>28</td><td>[3.195283, 0.715305, … 3.170495]</td><td>2.694864</td></tr></tbody></table></div></div>
+<small>shape: (5, 6)</small><table border="1" class="dataframe"><thead><tr><th>treeID</th><th>score</th><th>depth</th><th>nsplits</th><th>gains</th><th>meangains</th></tr><tr><td>i64</td><td>f64</td><td>i64</td><td>i64</td><td>list[f64]</td><td>f64</td></tr></thead><tbody><tr><td>38</td><td>-0.020387</td><td>7</td><td>20</td><td>[1.997382, 2.446064, … 0.397931]</td><td>1.921962</td></tr><tr><td>39</td><td>-0.018657</td><td>6</td><td>15</td><td>[0.120677, 0.47196, … 4.032721]</td><td>2.047581</td></tr><tr><td>35</td><td>-0.019854</td><td>8</td><td>34</td><td>[0.937961, 0.402603, … 0.536536]</td><td>1.590989</td></tr><tr><td>27</td><td>-0.021121</td><td>9</td><td>36</td><td>[0.056868, 0.394131, … 1.193229]</td><td>2.104693</td></tr><tr><td>24</td><td>-0.02452</td><td>7</td><td>34</td><td>[17.654567, 2.666297, … 2.200146]</td><td>4.338746</td></tr></tbody></table></div></div>
 </div>
 <p>In TreeStats, the index is the ‘ID’ of the tree, based on its position in the order of the ‘model’ attribute. The score corresponds to the score of the top-level node of that tree, and the ‘depth’ and ‘nsplits’ describe how deep the tree is, and how many splits are performed in total. For each split, the gain is added to the list in the ‘gains’ column. The mean of all splits in a tree is computed in the ‘meangains’ column.</p>
 <p>Some info about individual trees is also stored in attributes, such as the splits and gains for each tree.</p>
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19845.0&quot;</td><td>&quot;Customer.NetWealth&quot;</td><td>[[1.250128]]</td><td>1.250128</td><td>&quot;&lt;&quot;</td><td>{&#x27;19845.0&#x27;}</td><td>1</td></tr><tr><td>&quot;Customer.NetWealth &lt; 7557.0&quot;</td><td>&quot;Customer.NetWealth&quot;</td><td>[[0.180002]]</td><td>0.180002</td><td>&quot;&lt;&quot;</td><td>{&#x27;7557.0&#x27;}</td><td>1</td></tr><tr><td>&quot;Customer.RelationshipLengthDay…</td><td>&quot;Customer.RelationshipLengthDay…</td><td>[[2.206005]]</td><td>2.206005</td><td>&quot;&lt;&quot;</td><td>{&#x27;1122.0&#x27;}</td><td>1</td></tr><tr><td>&quot;Customer.NetWealth &lt; 18233.0&quot;</td><td>&quot;Customer.NetWealth&quot;</td><td>[[0.460809]]</td><td>0.460809</td><td>&quot;&lt;&quot;</td><td>{&#x27;18233.0&#x27;}</td><td>1</td></tr><tr><td>&quot;IH.Web.Inbound.Accepted.pxLast…</td><td>&quot;IH.Web.Inbound.Accepted.pxLast…</td><td>[[1.215137]]</td><td>1.215137</td><td>&quot;&lt;&quot;</td><td>{&#x27;0.9332240277777778&#x27;}</td><td>1</td></tr></tbody></table></div></div>
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19845.0&quot;</td><td>&quot;Customer.NetWealth&quot;</td><td>[[1.250128]]</td><td>1.250128</td><td>&quot;&lt;&quot;</td><td>{&#x27;19845.0&#x27;}</td><td>1</td></tr><tr><td>&quot;Customer.NetWealth &lt; 7557.0&quot;</td><td>&quot;Customer.NetWealth&quot;</td><td>[[0.180002]]</td><td>0.180002</td><td>&quot;&lt;&quot;</td><td>{&#x27;7557.0&#x27;}</td><td>1</td></tr><tr><td>&quot;Customer.RelationshipLengthDay…</td><td>&quot;Customer.RelationshipLengthDay…</td><td>[[2.206005]]</td><td>2.206005</td><td>&quot;&lt;&quot;</td><td>{&#x27;1122.0&#x27;}</td><td>1</td></tr><tr><td>&quot;Customer.NetWealth &lt; 18233.0&quot;</td><td>&quot;Customer.NetWealth&quot;</td><td>[[0.460809]]</td><td>0.460809</td><td>&quot;&lt;&quot;</td><td>{&#x27;18233.0&#x27;}</td><td>1</td></tr><tr><td>&quot;IH.Web.Inbound.Accepted.pxLast…</td><td>&quot;IH.Web.Inbound.Accepted.pxLast…</td><td>[[1.215137]]</td><td>1.215137</td><td>&quot;&lt;&quot;</td><td>{&#x27;0.9332240277777778&#x27;}</td><td>1</td></tr></tbody></table></div></div>
 </div>
 <p>Raw data is sometimes useful, but it’s better to visualise. For this, simply call plotSplitsPerVariable(), which will produce a plot of the distribution of splits for each variable. Here, the orange line denotes the number of times the given split is performed, while the blue boxes display the distribution of gains corresponding to that split. By suppling a set of predictors as the ‘subset’ argument, not all predictors are plotted. For readability’s sake, we’ve filtered only on a few specific
 splits.</p>
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 </div>
 <p>Now, to get to the final score we simply sum up the scores, and then normalize them to a range between 0 and 1:</p>
 <div class="nbinput docutils container">
@@ -1103,7 +1103,7 @@ <h2>Replicating scores<a class="headerlink" href="#Replicating-scores" title="Li
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-0.005635052249022455
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 </pre></div></div>
 </div>
 <p>And to simplify this even further, simply call the ‘score’ function to get the final score.</p>
@@ -1121,7 +1121,7 @@ <h2>Replicating scores<a class="headerlink" href="#Replicating-scores" title="Li
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-0.005635052249022455
+0.036858528035468414
 </pre></div></div>
 </div>
 <p>Finally, we can also plot the contribution of each tree towards the final propensity of the prediction. Simply call the plotContributionPerTree function with a given x. This shows, for each individual tree, the scores, the cumulative mean of those scores and the running propensity. Here you can clearly see that the average score is quite negative, so as we would expect the final propensity is also quite low.</p>
@@ -1137,9 +1137,9 @@ <h2>Replicating scores<a class="headerlink" href="#Replicating-scores" title="Li
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diff --git a/Python/articles/AGBModelVisualisation.ipynb b/Python/articles/AGBModelVisualisation.ipynb
index 3a061274..cfe37c78 100644
--- a/Python/articles/AGBModelVisualisation.ipynb
+++ b/Python/articles/AGBModelVisualisation.ipynb
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-     "iopub.status.busy": "2024-06-27T08:57:38.716305Z",
-     "iopub.status.idle": "2024-06-27T08:57:38.720940Z",
-     "shell.execute_reply": "2024-06-27T08:57:38.720452Z"
+     "iopub.execute_input": "2024-06-28T08:59:14.226925Z",
+     "iopub.status.busy": "2024-06-28T08:59:14.226609Z",
+     "iopub.status.idle": "2024-06-28T08:59:14.231098Z",
+     "shell.execute_reply": "2024-06-28T08:59:14.230542Z"
     }
    },
    "outputs": [
@@ -396,10 +396,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:38.722937Z",
-     "iopub.status.busy": "2024-06-27T08:57:38.722575Z",
-     "iopub.status.idle": "2024-06-27T08:57:38.726662Z",
-     "shell.execute_reply": "2024-06-27T08:57:38.726180Z"
+     "iopub.execute_input": "2024-06-28T08:59:14.233162Z",
+     "iopub.status.busy": "2024-06-28T08:59:14.232764Z",
+     "iopub.status.idle": "2024-06-28T08:59:14.236758Z",
+     "shell.execute_reply": "2024-06-28T08:59:14.236224Z"
     }
    },
    "outputs": [
@@ -448,10 +448,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:38.728526Z",
-     "iopub.status.busy": "2024-06-27T08:57:38.728356Z",
-     "iopub.status.idle": "2024-06-27T08:57:38.739985Z",
-     "shell.execute_reply": "2024-06-27T08:57:38.739416Z"
+     "iopub.execute_input": "2024-06-28T08:59:14.238662Z",
+     "iopub.status.busy": "2024-06-28T08:59:14.238358Z",
+     "iopub.status.idle": "2024-06-28T08:59:14.249784Z",
+     "shell.execute_reply": "2024-06-28T08:59:14.249335Z"
     }
    },
    "outputs": [
@@ -465,7 +465,7 @@
        "  white-space: pre-wrap;\n",
        "}\n",
        "</style>\n",
-       "<small>shape: (5, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>treeID</th><th>score</th><th>depth</th><th>nsplits</th><th>gains</th><th>meangains</th></tr><tr><td>i64</td><td>f64</td><td>i64</td><td>i64</td><td>list[f64]</td><td>f64</td></tr></thead><tbody><tr><td>4</td><td>-0.294892</td><td>9</td><td>25</td><td>[295.811814, 362.750664, … 4.187443]</td><td>30.616696</td></tr><tr><td>3</td><td>-0.32778</td><td>8</td><td>22</td><td>[210.495522, 3.745353, … 7.378691]</td><td>34.085356</td></tr><tr><td>49</td><td>-0.02314</td><td>8</td><td>31</td><td>[1.770591, 2.206005, … 4.969639]</td><td>2.431512</td></tr><tr><td>23</td><td>-0.025396</td><td>7</td><td>29</td><td>[15.175539, 0.007353, … 2.172846]</td><td>2.854175</td></tr><tr><td>33</td><td>-0.020448</td><td>9</td><td>28</td><td>[3.195283, 0.715305, … 3.170495]</td><td>2.694864</td></tr></tbody></table></div>"
+       "<small>shape: (5, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>treeID</th><th>score</th><th>depth</th><th>nsplits</th><th>gains</th><th>meangains</th></tr><tr><td>i64</td><td>f64</td><td>i64</td><td>i64</td><td>list[f64]</td><td>f64</td></tr></thead><tbody><tr><td>38</td><td>-0.020387</td><td>7</td><td>20</td><td>[1.997382, 2.446064, … 0.397931]</td><td>1.921962</td></tr><tr><td>39</td><td>-0.018657</td><td>6</td><td>15</td><td>[0.120677, 0.47196, … 4.032721]</td><td>2.047581</td></tr><tr><td>35</td><td>-0.019854</td><td>8</td><td>34</td><td>[0.937961, 0.402603, … 0.536536]</td><td>1.590989</td></tr><tr><td>27</td><td>-0.021121</td><td>9</td><td>36</td><td>[0.056868, 0.394131, … 1.193229]</td><td>2.104693</td></tr><tr><td>24</td><td>-0.02452</td><td>7</td><td>34</td><td>[17.654567, 2.666297, … 2.200146]</td><td>4.338746</td></tr></tbody></table></div>"
       ],
       "text/plain": [
        "shape: (5, 6)\n",
@@ -474,11 +474,11 @@
        "│ ---    ┆ ---       ┆ ---   ┆ ---     ┆ ---                             ┆ ---       │\n",
        "│ i64    ┆ f64       ┆ i64   ┆ i64     ┆ list[f64]                       ┆ f64       │\n",
        "╞════════╪═══════════╪═══════╪═════════╪═════════════════════════════════╪═══════════╡\n",
-       "│ 4      ┆ -0.294892 ┆ 9     ┆ 25      ┆ [295.811814, 362.750664, … 4.1… ┆ 30.616696 │\n",
-       "│ 3      ┆ -0.32778  ┆ 8     ┆ 22      ┆ [210.495522, 3.745353, … 7.378… ┆ 34.085356 │\n",
-       "│ 49     ┆ -0.02314  ┆ 8     ┆ 31      ┆ [1.770591, 2.206005, … 4.96963… ┆ 2.431512  │\n",
-       "│ 23     ┆ -0.025396 ┆ 7     ┆ 29      ┆ [15.175539, 0.007353, … 2.1728… ┆ 2.854175  │\n",
-       "│ 33     ┆ -0.020448 ┆ 9     ┆ 28      ┆ [3.195283, 0.715305, … 3.17049… ┆ 2.694864  │\n",
+       "│ 38     ┆ -0.020387 ┆ 7     ┆ 20      ┆ [1.997382, 2.446064, … 0.39793… ┆ 1.921962  │\n",
+       "│ 39     ┆ -0.018657 ┆ 6     ┆ 15      ┆ [0.120677, 0.47196, … 4.032721… ┆ 2.047581  │\n",
+       "│ 35     ┆ -0.019854 ┆ 8     ┆ 34      ┆ [0.937961, 0.402603, … 0.53653… ┆ 1.590989  │\n",
+       "│ 27     ┆ -0.021121 ┆ 9     ┆ 36      ┆ [0.056868, 0.394131, … 1.19322… ┆ 2.104693  │\n",
+       "│ 24     ┆ -0.02452  ┆ 7     ┆ 34      ┆ [17.654567, 2.666297, … 2.2001… ┆ 4.338746  │\n",
        "└────────┴───────────┴───────┴─────────┴─────────────────────────────────┴───────────┘"
       ]
      },
@@ -505,10 +505,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:38.742234Z",
-     "iopub.status.busy": "2024-06-27T08:57:38.741801Z",
-     "iopub.status.idle": "2024-06-27T08:57:38.754904Z",
-     "shell.execute_reply": "2024-06-27T08:57:38.754345Z"
+     "iopub.execute_input": "2024-06-28T08:59:14.251828Z",
+     "iopub.status.busy": "2024-06-28T08:59:14.251508Z",
+     "iopub.status.idle": "2024-06-28T08:59:14.264396Z",
+     "shell.execute_reply": "2024-06-28T08:59:14.263821Z"
     }
    },
    "outputs": [
@@ -549,10 +549,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:38.756917Z",
-     "iopub.status.busy": "2024-06-27T08:57:38.756531Z",
-     "iopub.status.idle": "2024-06-27T08:57:38.764266Z",
-     "shell.execute_reply": "2024-06-27T08:57:38.763723Z"
+     "iopub.execute_input": "2024-06-28T08:59:14.266275Z",
+     "iopub.status.busy": "2024-06-28T08:59:14.266022Z",
+     "iopub.status.idle": "2024-06-28T08:59:14.273793Z",
+     "shell.execute_reply": "2024-06-28T08:59:14.273265Z"
     }
    },
    "outputs": [
@@ -580,7 +580,7 @@
        "  white-space: pre-wrap;\n",
        "}\n",
        "</style>\n",
-       "<small>shape: (379, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>split</th><th>predictor</th><th>gains</th><th>mean</th><th>sign</th><th>values</th><th>n</th></tr><tr><td>str</td><td>str</td><td>list[list[f64]]</td><td>f64</td><td>str</td><td>object</td><td>u32</td></tr></thead><tbody><tr><td>&quot;pyName in { AutoUsed48Months, …</td><td>&quot;pyName&quot;</td><td>[[177.064524, 95.262737, … 6.283781]]</td><td>58.111616</td><td>&quot;in&quot;</td><td>{&#x27;VisaGold&#x27;, &#x27;AutoUsed84Months&#x27;, &#x27;StudentChoice&#x27;, &#x27;IndividualRetirementAccountsIRA&#x27;, &#x27;PremiumBanking&#x27;, &#x27;IncreaseYourCreditLine&#x27;, &#x27;SuperSaver&#x27;, &#x27;MasterCardWorld&#x27;, &#x27;PlatinumRewardsCard&#x27;, &#x27;PremierChecking&#x27;, &#x27;CompleteYourCardApplicationToday&#x27;, &#x27;AMEXPersonal&#x27;, &#x27;CreditMonitoringService&#x27;, &#x27;GetAPersonalizedRateQuoteToday&#x27;, &#x27;ProsAndConsOfFixedRate&#x27;, &#x27;UPlusGold&#x27;, &#x27;UFixedRateMortgage&#x27;, &#x27;PaymentProtection&#x27;, &#x27;FirstMortgage&#x27;, &#x27;FirstMortgageFloat&#x27;, &#x27;HomeOwners&#x27;, &#x27;FirstMortgageFiveOneARM&#x27;, &#x27;AutoNew60Months&#x27;, &#x27;MoneyMarketSavingsAccount&#x27;, &#x27;FirstMortgage30yr&#x27;, &#x27;AutoNew84Months&#x27;, &#x27;UPlusProductBundles&#x27;, &#x27;SignupForRewardsCard&#x27;, &#x27;AutoUsed36Months&#x27;, &#x27;MasterCardGold&#x27;, &#x27;BasicChecking&#x27;, &#x27;Earn2xRewardsPoints&#x27;, &#x27;IdentityTheftProtection&#x27;, &#x27;AutoUsed48Months&#x27;, &#x27;FirstMortgageSevenOneARM&#x27;}</td><td>1</td></tr><tr><td>&quot;pyName in { AutoUsed48Months, …</td><td>&quot;pyName&quot;</td><td>[[1102.556954, 153.075758, … 2.770062]]</td><td>144.75059</td><td>&quot;in&quot;</td><td>{&#x27;VisaGold&#x27;, &#x27;AutoUsed84Months&#x27;, &#x27;StudentChoice&#x27;, &#x27;IndividualRetirementAccountsIRA&#x27;, &#x27;PremiumBanking&#x27;, &#x27;IncreaseYourCreditLine&#x27;, &#x27;SuperSaver&#x27;, &#x27;MasterCardWorld&#x27;, &#x27;PlatinumRewardsCard&#x27;, &#x27;CompleteYourCardApplicationToday&#x27;, &#x27;AMEXPersonal&#x27;, &#x27;CreditMonitoringService&#x27;, &#x27;GetAPersonalizedRateQuoteToday&#x27;, &#x27;ProsAndConsOfFixedRate&#x27;, &#x27;UPlusGold&#x27;, &#x27;UFixedRateMortgage&#x27;, &#x27;PaymentProtection&#x27;, &#x27;FirstMortgage&#x27;, &#x27;FirstMortgageFloat&#x27;, &#x27;HomeOwners&#x27;, &#x27;FirstMortgageFiveOneARM&#x27;, &#x27;AutoNew60Months&#x27;, &#x27;FirstMortgage30yr&#x27;, &#x27;AutoNew84Months&#x27;, &#x27;UPlusProductBundles&#x27;, &#x27;SignupForRewardsCard&#x27;, &#x27;AutoUsed36Months&#x27;, &#x27;MasterCardGold&#x27;, &#x27;Earn2xRewardsPoints&#x27;, &#x27;IdentityTheftProtection&#x27;, &#x27;AutoUsed48Months&#x27;, &#x27;FirstMortgageSevenOneARM&#x27;}</td><td>1</td></tr><tr><td>&quot;pyName in { UPlusGold }&quot;</td><td>&quot;pyName&quot;</td><td>[[3.648124, 5.092834, … 1.945262]]</td><td>3.336998</td><td>&quot;in&quot;</td><td>{&#x27;UPlusGold&#x27;}</td><td>1</td></tr><tr><td>&quot;pyName in { AutoUsed48Months, …</td><td>&quot;pyName&quot;</td><td>[[1.687369, 3.857415, … 2.235231]]</td><td>1.985773</td><td>&quot;in&quot;</td><td>{&#x27;VisaGold&#x27;, &#x27;AutoUsed84Months&#x27;, &#x27;StudentChoice&#x27;, &#x27;PremiumBanking&#x27;, &#x27;IncreaseYourCreditLine&#x27;, &#x27;SuperSaver&#x27;, &#x27;MasterCardWorld&#x27;, &#x27;PlatinumRewardsCard&#x27;, &#x27;CompleteYourCardApplicationToday&#x27;, &#x27;AMEXPersonal&#x27;, &#x27;UPlusGold&#x27;, &#x27;FirstMortgage&#x27;, &#x27;FirstMortgageFloat&#x27;, &#x27;HomeOwners&#x27;, &#x27;FirstMortgageFiveOneARM&#x27;, &#x27;AutoNew60Months&#x27;, &#x27;FirstMortgage30yr&#x27;, &#x27;AutoNew84Months&#x27;, &#x27;UPlusProductBundles&#x27;, &#x27;AutoUsed36Months&#x27;, &#x27;MasterCardGold&#x27;, &#x27;Earn2xRewardsPoints&#x27;, &#x27;AutoUsed48Months&#x27;, &#x27;FirstMortgageSevenOneARM&#x27;}</td><td>1</td></tr><tr><td>&quot;IH.Web.Inbound.Rejected.pxLast…</td><td>&quot;IH.Web.Inbound.Rejected.pxLast…</td><td>[[5.216521, 9.372519, 4.885585]]</td><td>6.491542</td><td>&quot;&lt;&quot;</td><td>{&#x27;0.9332044675925926&#x27;}</td><td>1</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;Customer.NetWealth &lt; 19845.0&quot;</td><td>&quot;Customer.NetWealth&quot;</td><td>[[1.250128]]</td><td>1.250128</td><td>&quot;&lt;&quot;</td><td>{&#x27;19845.0&#x27;}</td><td>1</td></tr><tr><td>&quot;Customer.NetWealth &lt; 7557.0&quot;</td><td>&quot;Customer.NetWealth&quot;</td><td>[[0.180002]]</td><td>0.180002</td><td>&quot;&lt;&quot;</td><td>{&#x27;7557.0&#x27;}</td><td>1</td></tr><tr><td>&quot;Customer.RelationshipLengthDay…</td><td>&quot;Customer.RelationshipLengthDay…</td><td>[[2.206005]]</td><td>2.206005</td><td>&quot;&lt;&quot;</td><td>{&#x27;1122.0&#x27;}</td><td>1</td></tr><tr><td>&quot;Customer.NetWealth &lt; 18233.0&quot;</td><td>&quot;Customer.NetWealth&quot;</td><td>[[0.460809]]</td><td>0.460809</td><td>&quot;&lt;&quot;</td><td>{&#x27;18233.0&#x27;}</td><td>1</td></tr><tr><td>&quot;IH.Web.Inbound.Accepted.pxLast…</td><td>&quot;IH.Web.Inbound.Accepted.pxLast…</td><td>[[1.215137]]</td><td>1.215137</td><td>&quot;&lt;&quot;</td><td>{&#x27;0.9332240277777778&#x27;}</td><td>1</td></tr></tbody></table></div>"
+       "<small>shape: (379, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>split</th><th>predictor</th><th>gains</th><th>mean</th><th>sign</th><th>values</th><th>n</th></tr><tr><td>str</td><td>str</td><td>list[list[f64]]</td><td>f64</td><td>str</td><td>object</td><td>u32</td></tr></thead><tbody><tr><td>&quot;pyName in { AutoUsed48Months, …</td><td>&quot;pyName&quot;</td><td>[[177.064524, 95.262737, … 6.283781]]</td><td>58.111616</td><td>&quot;in&quot;</td><td>{&#x27;Earn2xRewardsPoints&#x27;, &#x27;UPlusProductBundles&#x27;, &#x27;FirstMortgageSevenOneARM&#x27;, &#x27;MasterCardWorld&#x27;, &#x27;MasterCardGold&#x27;, &#x27;StudentChoice&#x27;, &#x27;AutoUsed36Months&#x27;, &#x27;SuperSaver&#x27;, &#x27;SignupForRewardsCard&#x27;, &#x27;PremierChecking&#x27;, &#x27;AutoNew60Months&#x27;, &#x27;CompleteYourCardApplicationToday&#x27;, &#x27;HomeOwners&#x27;, &#x27;VisaGold&#x27;, &#x27;GetAPersonalizedRateQuoteToday&#x27;, &#x27;CreditMonitoringService&#x27;, &#x27;FirstMortgageFiveOneARM&#x27;, &#x27;PremiumBanking&#x27;, &#x27;IndividualRetirementAccountsIRA&#x27;, &#x27;FirstMortgage30yr&#x27;, &#x27;UPlusGold&#x27;, &#x27;FirstMortgage&#x27;, &#x27;AMEXPersonal&#x27;, &#x27;IdentityTheftProtection&#x27;, &#x27;IncreaseYourCreditLine&#x27;, &#x27;AutoUsed84Months&#x27;, &#x27;MoneyMarketSavingsAccount&#x27;, &#x27;AutoNew84Months&#x27;, &#x27;ProsAndConsOfFixedRate&#x27;, &#x27;PlatinumRewardsCard&#x27;, &#x27;AutoUsed48Months&#x27;, &#x27;PaymentProtection&#x27;, &#x27;BasicChecking&#x27;, &#x27;UFixedRateMortgage&#x27;, &#x27;FirstMortgageFloat&#x27;}</td><td>1</td></tr><tr><td>&quot;pyName in { AutoUsed48Months, …</td><td>&quot;pyName&quot;</td><td>[[1102.556954, 153.075758, … 2.770062]]</td><td>144.75059</td><td>&quot;in&quot;</td><td>{&#x27;Earn2xRewardsPoints&#x27;, &#x27;UPlusProductBundles&#x27;, &#x27;FirstMortgageSevenOneARM&#x27;, &#x27;MasterCardWorld&#x27;, &#x27;MasterCardGold&#x27;, &#x27;StudentChoice&#x27;, &#x27;AutoUsed36Months&#x27;, &#x27;SuperSaver&#x27;, &#x27;SignupForRewardsCard&#x27;, &#x27;AutoNew60Months&#x27;, &#x27;CompleteYourCardApplicationToday&#x27;, &#x27;HomeOwners&#x27;, &#x27;VisaGold&#x27;, &#x27;GetAPersonalizedRateQuoteToday&#x27;, &#x27;CreditMonitoringService&#x27;, &#x27;FirstMortgageFiveOneARM&#x27;, &#x27;PremiumBanking&#x27;, &#x27;IndividualRetirementAccountsIRA&#x27;, &#x27;FirstMortgage30yr&#x27;, &#x27;UPlusGold&#x27;, &#x27;FirstMortgage&#x27;, &#x27;AMEXPersonal&#x27;, &#x27;IdentityTheftProtection&#x27;, &#x27;IncreaseYourCreditLine&#x27;, &#x27;AutoUsed84Months&#x27;, &#x27;AutoNew84Months&#x27;, &#x27;ProsAndConsOfFixedRate&#x27;, &#x27;PlatinumRewardsCard&#x27;, &#x27;AutoUsed48Months&#x27;, &#x27;PaymentProtection&#x27;, &#x27;UFixedRateMortgage&#x27;, &#x27;FirstMortgageFloat&#x27;}</td><td>1</td></tr><tr><td>&quot;pyName in { UPlusGold }&quot;</td><td>&quot;pyName&quot;</td><td>[[3.648124, 5.092834, … 1.945262]]</td><td>3.336998</td><td>&quot;in&quot;</td><td>{&#x27;UPlusGold&#x27;}</td><td>1</td></tr><tr><td>&quot;pyName in { AutoUsed48Months, …</td><td>&quot;pyName&quot;</td><td>[[1.687369, 3.857415, … 2.235231]]</td><td>1.985773</td><td>&quot;in&quot;</td><td>{&#x27;Earn2xRewardsPoints&#x27;, &#x27;UPlusProductBundles&#x27;, &#x27;FirstMortgageSevenOneARM&#x27;, &#x27;MasterCardWorld&#x27;, &#x27;MasterCardGold&#x27;, &#x27;StudentChoice&#x27;, &#x27;AutoUsed36Months&#x27;, &#x27;SuperSaver&#x27;, &#x27;AutoNew60Months&#x27;, &#x27;CompleteYourCardApplicationToday&#x27;, &#x27;HomeOwners&#x27;, &#x27;VisaGold&#x27;, &#x27;FirstMortgageFiveOneARM&#x27;, &#x27;PremiumBanking&#x27;, &#x27;FirstMortgage30yr&#x27;, &#x27;UPlusGold&#x27;, &#x27;FirstMortgage&#x27;, &#x27;AMEXPersonal&#x27;, &#x27;IncreaseYourCreditLine&#x27;, &#x27;AutoUsed84Months&#x27;, &#x27;AutoNew84Months&#x27;, &#x27;PlatinumRewardsCard&#x27;, &#x27;AutoUsed48Months&#x27;, &#x27;FirstMortgageFloat&#x27;}</td><td>1</td></tr><tr><td>&quot;IH.Web.Inbound.Rejected.pxLast…</td><td>&quot;IH.Web.Inbound.Rejected.pxLast…</td><td>[[5.216521, 9.372519, 4.885585]]</td><td>6.491542</td><td>&quot;&lt;&quot;</td><td>{&#x27;0.9332044675925926&#x27;}</td><td>1</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;Customer.NetWealth &lt; 19845.0&quot;</td><td>&quot;Customer.NetWealth&quot;</td><td>[[1.250128]]</td><td>1.250128</td><td>&quot;&lt;&quot;</td><td>{&#x27;19845.0&#x27;}</td><td>1</td></tr><tr><td>&quot;Customer.NetWealth &lt; 7557.0&quot;</td><td>&quot;Customer.NetWealth&quot;</td><td>[[0.180002]]</td><td>0.180002</td><td>&quot;&lt;&quot;</td><td>{&#x27;7557.0&#x27;}</td><td>1</td></tr><tr><td>&quot;Customer.RelationshipLengthDay…</td><td>&quot;Customer.RelationshipLengthDay…</td><td>[[2.206005]]</td><td>2.206005</td><td>&quot;&lt;&quot;</td><td>{&#x27;1122.0&#x27;}</td><td>1</td></tr><tr><td>&quot;Customer.NetWealth &lt; 18233.0&quot;</td><td>&quot;Customer.NetWealth&quot;</td><td>[[0.460809]]</td><td>0.460809</td><td>&quot;&lt;&quot;</td><td>{&#x27;18233.0&#x27;}</td><td>1</td></tr><tr><td>&quot;IH.Web.Inbound.Accepted.pxLast…</td><td>&quot;IH.Web.Inbound.Accepted.pxLast…</td><td>[[1.215137]]</td><td>1.215137</td><td>&quot;&lt;&quot;</td><td>{&#x27;0.9332240277777778&#x27;}</td><td>1</td></tr></tbody></table></div>"
       ],
       "text/plain": [
        "shape: (379, 7)\n",
@@ -589,18 +589,18 @@
        "│ ---              ┆ ---              ┆ ---             ┆ ---       ┆ ---  ┆ ---             ┆ --- │\n",
        "│ str              ┆ str              ┆ list[list[f64]] ┆ f64       ┆ str  ┆ object          ┆ u32 │\n",
        "╞══════════════════╪══════════════════╪═════════════════╪═══════════╪══════╪═════════════════╪═════╡\n",
-       "│ pyName in { Auto ┆ pyName           ┆ [[177.064524,   ┆ 58.111616 ┆ in   ┆ {'VisaGold',    ┆ 1   │\n",
-       "│ Used48Months, …  ┆                  ┆ 95.262737, …    ┆           ┆      ┆ 'AutoUsed84Mont ┆     │\n",
-       "│                  ┆                  ┆ 6.2…            ┆           ┆      ┆ hs…             ┆     │\n",
-       "│ pyName in { Auto ┆ pyName           ┆ [[1102.556954,  ┆ 144.75059 ┆ in   ┆ {'VisaGold',    ┆ 1   │\n",
-       "│ Used48Months, …  ┆                  ┆ 153.075758, …   ┆           ┆      ┆ 'AutoUsed84Mont ┆     │\n",
-       "│                  ┆                  ┆ 2…              ┆           ┆      ┆ hs…             ┆     │\n",
+       "│ pyName in { Auto ┆ pyName           ┆ [[177.064524,   ┆ 58.111616 ┆ in   ┆ {'Earn2xRewards ┆ 1   │\n",
+       "│ Used48Months, …  ┆                  ┆ 95.262737, …    ┆           ┆      ┆ Points',        ┆     │\n",
+       "│                  ┆                  ┆ 6.2…            ┆           ┆      ┆ 'UPlus…         ┆     │\n",
+       "│ pyName in { Auto ┆ pyName           ┆ [[1102.556954,  ┆ 144.75059 ┆ in   ┆ {'Earn2xRewards ┆ 1   │\n",
+       "│ Used48Months, …  ┆                  ┆ 153.075758, …   ┆           ┆      ┆ Points',        ┆     │\n",
+       "│                  ┆                  ┆ 2…              ┆           ┆      ┆ 'UPlus…         ┆     │\n",
        "│ pyName in {      ┆ pyName           ┆ [[3.648124,     ┆ 3.336998  ┆ in   ┆ {'UPlusGold'}   ┆ 1   │\n",
        "│ UPlusGold }      ┆                  ┆ 5.092834, …     ┆           ┆      ┆                 ┆     │\n",
        "│                  ┆                  ┆ 1.9452…         ┆           ┆      ┆                 ┆     │\n",
-       "│ pyName in { Auto ┆ pyName           ┆ [[1.687369,     ┆ 1.985773  ┆ in   ┆ {'VisaGold',    ┆ 1   │\n",
-       "│ Used48Months, …  ┆                  ┆ 3.857415, …     ┆           ┆      ┆ 'AutoUsed84Mont ┆     │\n",
-       "│                  ┆                  ┆ 2.2352…         ┆           ┆      ┆ hs…             ┆     │\n",
+       "│ pyName in { Auto ┆ pyName           ┆ [[1.687369,     ┆ 1.985773  ┆ in   ┆ {'Earn2xRewards ┆ 1   │\n",
+       "│ Used48Months, …  ┆                  ┆ 3.857415, …     ┆           ┆      ┆ Points',        ┆     │\n",
+       "│                  ┆                  ┆ 2.2352…         ┆           ┆      ┆ 'UPlus…         ┆     │\n",
        "│ IH.Web.Inbound.R ┆ IH.Web.Inbound.R ┆ [[5.216521,     ┆ 6.491542  ┆ <    ┆ {'0.93320446759 ┆ 1   │\n",
        "│ ejected.pxLast…  ┆ ejected.pxLast…  ┆ 9.372519,       ┆           ┆      ┆ 25926'}         ┆     │\n",
        "│                  ┆                  ┆ 4.885585…       ┆           ┆      ┆                 ┆     │\n",
@@ -642,10 +642,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:38.766214Z",
-     "iopub.status.busy": "2024-06-27T08:57:38.766049Z",
-     "iopub.status.idle": "2024-06-27T08:57:38.842189Z",
-     "shell.execute_reply": "2024-06-27T08:57:38.841673Z"
+     "iopub.execute_input": "2024-06-28T08:59:14.275688Z",
+     "iopub.status.busy": "2024-06-28T08:59:14.275400Z",
+     "iopub.status.idle": "2024-06-28T08:59:14.348764Z",
+     "shell.execute_reply": "2024-06-28T08:59:14.348188Z"
     }
    },
    "outputs": [
@@ -676,9 +676,9 @@
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PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA, BasicChecking, MoneyMarketSavingsAccount }\",\"pyName in { MoneyMarketSavingsAccount }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months }\",\"pyName in { SeniorChecking }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA, BasicChecking }\",\"pyName in { BasicChecking }\",\"pyName in { FirstMortgageSevenOneARM }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA, BasicChecking, MoneyMarketSavingsAccount }\",\"pyName in { SeniorChecking }\",\"pyName in { PremierChecking }\",\"pyName in { SeniorChecking }\",\"pyName in { PlatinumRewardsCard }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, 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MoneyMarketSavingsAccount }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM }\",\"pyName in { PremierChecking 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MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA, BasicChecking }\",\"pyName in { UPlusGold }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat }\",\"pyName in { PlatinumRewardsCard }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months }\",\"pyName in { PlatinumRewardsCard }\",\"pyName in { CompleteYourCardApplicationToday }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard }\",\"pyName in { Earn2xRewardsPoints }\",\"pyName in { PremierChecking }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA, BasicChecking, MoneyMarketSavingsAccount }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM }\",\"pyName in { PlatinumRewardsCard }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA, BasicChecking, MoneyMarketSavingsAccount }\",\"pyName in { BasicChecking }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA, BasicChecking }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA, BasicChecking }\",\"pyName in { MoneyMarketSavingsAccount }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA, BasicChecking, MoneyMarketSavingsAccount }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA, BasicChecking }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA }\",\"pyName in { PlatinumRewardsCard }\",\"pyName in { MoneyMarketSavingsAccount }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService }\",\"pyName in { PremierChecking }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA, BasicChecking, MoneyMarketSavingsAccount, PremierChecking }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA, BasicChecking }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA, BasicChecking }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, 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CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService }\",\"pyName in { PremierChecking }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA, BasicChecking, MoneyMarketSavingsAccount, PremierChecking }\",\"pyName in { 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AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, FirstMortgageFloat, AutoUsed36Months, UPlusProductBundles, MasterCardGold, AutoNew84Months, MasterCardWorld, FirstMortgageFiveOneARM, FirstMortgage, VisaGold, FirstMortgage30yr, PremiumBanking, IncreaseYourCreditLine, AutoNew60Months, SuperSaver, Earn2xRewardsPoints, CompleteYourCardApplicationToday, PlatinumRewardsCard, CreditMonitoringService, IdentityTheftProtection, PaymentProtection, SignupForRewardsCard, ProsAndConsOfFixedRate, GetAPersonalizedRateQuoteToday, UFixedRateMortgage, IndividualRetirementAccountsIRA, BasicChecking }\",\"pyName in { AutoUsed48Months, HomeOwners, UPlusGold, FirstMortgageSevenOneARM, AutoUsed84Months, StudentChoice, AMEXPersonal, 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-     "iopub.execute_input": "2024-06-27T08:57:38.850096Z",
-     "iopub.status.busy": "2024-06-27T08:57:38.849781Z",
-     "iopub.status.idle": "2024-06-27T08:57:39.137681Z",
-     "shell.execute_reply": "2024-06-27T08:57:39.137139Z"
+     "iopub.execute_input": "2024-06-28T08:59:14.356814Z",
+     "iopub.status.busy": "2024-06-28T08:59:14.356410Z",
+     "iopub.status.idle": "2024-06-28T08:59:14.435039Z",
+     "shell.execute_reply": "2024-06-28T08:59:14.434464Z"
     }
    },
    "outputs": [
@@ -889,10 +889,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:39.139941Z",
-     "iopub.status.busy": "2024-06-27T08:57:39.139627Z",
-     "iopub.status.idle": "2024-06-27T08:57:39.205411Z",
-     "shell.execute_reply": "2024-06-27T08:57:39.204878Z"
+     "iopub.execute_input": "2024-06-28T08:59:14.437400Z",
+     "iopub.status.busy": "2024-06-28T08:59:14.437042Z",
+     "iopub.status.idle": "2024-06-28T08:59:14.512742Z",
+     "shell.execute_reply": "2024-06-28T08:59:14.512057Z"
     }
    },
    "outputs": [
@@ -923,10 +923,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:39.207606Z",
-     "iopub.status.busy": "2024-06-27T08:57:39.207191Z",
-     "iopub.status.idle": "2024-06-27T08:57:39.271756Z",
-     "shell.execute_reply": "2024-06-27T08:57:39.271215Z"
+     "iopub.execute_input": "2024-06-28T08:59:14.515333Z",
+     "iopub.status.busy": "2024-06-28T08:59:14.514856Z",
+     "iopub.status.idle": "2024-06-28T08:59:14.582644Z",
+     "shell.execute_reply": "2024-06-28T08:59:14.582124Z"
     }
    },
    "outputs": [
@@ -958,16 +958,16 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:39.273816Z",
-     "iopub.status.busy": "2024-06-27T08:57:39.273467Z",
-     "iopub.status.idle": "2024-06-27T08:57:40.199428Z",
-     "shell.execute_reply": "2024-06-27T08:57:40.198849Z"
+     "iopub.execute_input": "2024-06-28T08:59:14.584821Z",
+     "iopub.status.busy": "2024-06-28T08:59:14.584498Z",
+     "iopub.status.idle": "2024-06-28T08:59:15.503783Z",
+     "shell.execute_reply": "2024-06-28T08:59:15.503182Z"
     }
    },
    "outputs": [
     {
      "data": {
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Rbf1SW22oPlGp3hzY1qvT6cTnT0Q3J9af6GaoP0njjO12O3w+H7RaLdLT0/GBD3wAubm5WLRoEXp7e/Haa6+x/kS0gASPh5luH2W48Uuq50hlX2lsYUxMjBjnkJmZKRsTI9W1QsWz4GseAutRwX2RHDczWWD9KjhuBV/PIH2+0rjUUH2V0rhjKdYNDQ3BbrfL4pUU2wKvt5iKRqOBUqkUdS2p/h3cDynVuabqowysWxMR0RVqtRoHDhzAAw88gMOHD+Pw4cNIS0vDnXfeiXvuuQcf+tCHJp0/u7u7cfvtt8PtduPZZ5/Fpk2bMDIygmeffRaf/vSn0d/fj4ceekikGxkZwXPPPYeqqip0d3fjkUcewR133IGf//zn2LNnDz784Q/D7/fjwx/+MF599VV87nOfw7e//W288MILOH/+PNavXw8A6Orqwrp16+DxePD888+jqqoKbW1tePDBB7Fu3TocPXoU69atE/ncunUrzp07h9dffx2VlZXX9f5IbYGJiYkh3zvg/fg3k/vZvn07Dh06hEOHDuEDH/iALP3hw4cBAMPDw1d9zZ6eHvzbv/0b9uzZg4997GPXzKPf78dTTz2Fhx566Jppryf9dPLjdDrx/PPPo6ioCFarFd/97nfxiU98Au+++y5+8pOfyNLu2bMHDzzwALZu3Tqt1//CF76AjIwMfOlLX8L3vvc9AEBKSgqee+45fOYzn5GlDff9n+304Zqv359wPl8iijyWEa6YqbJAKNc614Yb68JJP9uxKNC+ffvw2GOPAQA2b96Ml19+GUuXLg2ZNpxYEU5sB2b3/bye9MFm8vsQ7uc7V2VVlgWIiIiIiIiIiIhuDtI4wcCx74HXZgWOfQ+cZ9ntdmNoaAiDg4MYHR2VjUGbSuD4wcAxYtJ49tjYWJhMJgDvj2cPHiM4nfHxwOQ5K4Ov1aIrgucaCRxbGGrOEqmNOXh8fKjxhdJYQbvdjvHxcQwODsLj8WBwcFA238m1BH5X1Gq1GE8Yak4TjUYjrumS/g4cY0hEt45IzM+lVCqRlJQk4g3n55p7nJ+LiIiIiIiIiIiIiIiI5gJnWSQiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiolvGyMgILl++jL6+PrH09vbKnpPWA29yP9XNf+Pj48WNxKWbiut0OqSkpKCgoAAJCQnQ6/VISEiAUqkMua5SqaDT6cRNginyVCoVVCqV+Fu6SfNcCbzp9FTrg4OD8Hg8GBwcFDet7uvrg8ViEX8PDAygv78ffr9/0mtIN5SWbiys0+mQnJyM5ORkpKSkiPVFixZNWo+N5TT2RNdreHhYxBuHwyGLR8GL9DseGBiY8qbi0u9XWqS/CwsLodfroVaroVKpoNFooNFooFKpoFarodVqoVKpkJiYCJ1OJ25WT5ETHR0tizdzHXv6+/vhdrvhdrvhdDrFen9/P0ZGRuB2uzEwMIDh4WGMjIyI76bFYpGVmQYGBuB2u0O+hkajkd3YXoovUoxJTU2VPSct8fHxc/peENH84vV6ZfW04PXe3l7xXH9/P1wuF/r7+zEwMICJiYmQ+0xKSpLFTenRYDBAp9NBo9FAqVSK+HmtdYq8mJiYiMbR0dFRER/dbvc114eHh0XctNvt6O/vF9/fgYEBjI2NhXydwFgq1eFC1dmC63Qs5xHNnLGxMRGHLl++PGk9sJ0xsG3G5XLB6/WG3KdUR5NiktTOaDQaUVZWhoSEBKjValnsUavVUCqV0Gq1SExMhFKpZPviPBXpdka/3y/qex6PB06nEx6PR9T3PB6PiE8ejwfDw8PiO+twONDc3DyttkYAk8pXUhyS4lNgzAr8n1qtntP3hIjoVuH1ekUZJHgJLJ9Ii8vlEsvIyEjIfcbGxsr6PLVaraiHSHXmwLKHSqWCUqlEUlISFAoFEhISoNPpoFQqkZiYCI1Gwz6leUqn08n+TklJmfM8SH0fQ0NDsn7P4eFheDwe0f6s1+tFeUb6DlssFlFmuZ7vtVQOCa5TBxnYblQAACAASURBVC+sTxPNnLGxsZBtu1L7buAS2E421VgdKRYFLklJSaIunZiYOKkfVK/XizpaUlKSWNfr9XP8blAwrVYr+zstLW3OXju4H1SKN8Hjc9xuN4aGhsT3s6enBxcvXpT16btcrpCvoVAoZGPLQvWHBo7XSUlJYX2ZaIax/kQ34mapP7ndbnR0dAAAxsfHxXe4paUFZ8+eZf2J6CYljTcNvvYhVByTyqTSuJlQpLHjgf2S0lJQUDDpOofA+KVUKkUdKrA+FRUVNcfvCl1NcP1qruNW4PgZqe/R5XLB4/FMimNDQ0MYGRkRbQCtra2yeHW1a3iCY5ZOpwvZFxmqv5LjUYnoVrZhwwY0NTXh97//Pf74xz/i6NGj+M1vfoPf/OY3SE5OxpNPPon7779fpH/kkUfQ0tKC3//+97j33nsBXBmj+Oijj+LkyZOT0v3P//yPSKfVavHb3/4WJpMJX/jCF7Bjxw6kp6fL8vP1r38dmzdvBgCsXbtWjJ36x3/8R7S1teE3v/kNPvjBDwIAysrK8Lvf/Q55eXn4/Oc/j9OnT4v9TExMwO/3Tzle5UZJ+73Rck2o/ezZswcvvPACnnnmGRQVFeH+++/H8PAw/vM//xOdnZ0AIBtXFKyvrw933XUXNm/ejGeeeWZa+XjjjTfQ1dWFT33qUzOefjr5qa6ulv1dVFSEF198EWazGT/96U/xiU98AmvXrgUAPPvss2hqasIf/vCHaeXV7/fjc5/7HF544QU88cQTeOCBBxATE4M//vGP+Nd//Vf8+c9/xv79+0UbS7jv/2ynD9d8/P6E8/kS0fzBMsLV3UhZ4Frn2nBjXbjpZzsWBXr00Ufxta99DRaLBT/+8Y9RXl6Ob33rW3jsscdk6cKJFeHG9tl+P8NNH2ymvw8z+fnOVFmVZQEiIiIiIiIiIqK5Jc0vG2rMe/AiXYd1tTGEAKDX68W498AlLy8PWq0WCQkJYny7NL/JVOPipXGFNP8kJCTIxnfP9TwnAGRznASOLZxqXLx0TZfT6YTVahVjCK81NhaQf6+l8YRXW6TxhLyukGh2cH4uChfn5+L8XERERERERERERERERHOBMz0SEREREREREREREREREREREREREREREREREREREREREREREREREREREREREdG8NTg4CJvNBofDgZ6eHnR1dcHhcMBut6O3t1d20/u+vj643W7Z9lFRUbKbo0rrixcvhk6nk91YVafTQa/Xy55TKpUROnK6lanVaqjVaqSmps7I/qQb3w8MDMge+/v7xU2H+/v7cfnyZbS3t+PMmTPitzM0NDRpfzqdDsnJyUhJSZH9flJTU5GZmYm0tDSkpaUhIyMDqamp/J3QLW14eBhdXV3o6emB3W6HzWaD3W6Hw+GAw+EQvyVp8Xg8su1jY2Mn3aA7KysLy5cvFzEneJFuWK/X6xEVFRWhI6dbjfSdmgljY2PiBvYDAwNwOp2yGCTd2L6vrw92ux0NDQ3o6+uDw+HAwMDApP1pNBoRZwJ/L2lpaSLuGAwGGAwGpKWlIS4ubkaOg4hmx+joKOx2O7q6umC320X8DIybly9fFvW5UOcFtVot4mZqaioWLVqEpUuXQq/Xy+KkVqsVdbfAR6KZplAooFAokJSUNCP783g8stjZ398vYmhgPJV+L01NTbLfj9/vl+1PpVLJ2jyk+lxaWhpSU1ORkZEhq8fxd0ILicvlkrUnSutSXJJi0uXLl3H58mUMDg5O2kdgXJJ+Y6WlpbL2RCkmBS5SnS86OjoCR04LRVRUFJKSkmYsRgFX2uSl9kZpkWJV4N/S78dsNovfUF9f36Q4pVAosGjRIvEbkh5TUlJgMBiQmpqK9PR0pKeni9jF3w0R3YouX74s2pbtdju6u7tFuUQ6hwY+ulyuSftQqVTinCqdT3NycrBy5UrodDpoNBpRFtFoNEhKSpKVT1QqVQSOnBYqnU4HAEhLS5uR/fl8PrhcLjidTlEmCSy3BJZX+vr60NHRgXPnzonfVKg2KKVSOek3tWjRIqSkpCA9PR2pqamifTo1NRWpqamIjeXtwGlhGB8fF/Gqu7tb1Kl7enpkfaO9vb3o7e0NWZ/W6XST+n4KCgomjcvR6XRISkqS9ZUqFIoIHDXdihISEpCQkIDk5OQb3pff75e15QYvUjySfh8dHR2y34vX65XtT6FQyH4fUn9pYFuuVF/OyMhAYmLiDR8D0c2C9SdaaFh/Iro1TExMiHjV09Mj4ldPT4+sbzIwho2Ojsr2EXjdQ+DvzWQyhbzmIbh/UqPRROjoaaGY6fEzwfWsqfoopTE0ra2tOH36tPgdhboOIlT/vlTfChxDI61zDA0R3WwUCgV2796N3bt3w+v14sSJE3j22Wfxu9/9Dp/85CdRVFSE8vJyAMDLL78MALj77rsn7eeNN94Q61K6e+65Z9Jr3XHHHfj1r3+NN998E7t375b9/7bbbguZx1deeQXR0dG49957Zc8bDAaUlZWhpqYGVqsV2dnZAIBjx45N69gvXLiAZcuWyZ578MEH8eSTT4rrJIaHhydtJz03nWspwt2PUqnE22+/jccffxw//OEP8fDDDyM5ORm7du3CgQMHsHHjRhgMhpCvNTw8jO3bt6O0tBQvvvgiYmJirpk/APjJT36C3bt3Q61Wz2j6682P5CMf+Qj+9re/4eDBg1i7di3a29vx1a9+Fa+++uq02zZ//etf49lnn8XnP/95PPzww+L5z372s+ju7sa3vvUtVFZW4qGHHgIQ/vs/2+nDdTN8fyTBny8RzT8LuYwAhB/Dp+Na59pwY931xMbZjkXB4uPjUVxcjKeffho9PT345je/iXXr1mHbtm3X3DZUrAgnts/2+3k973+gmf4+AOF/vnNZVg3GsgAREREREREREdG1jYyMwOFwyOY0ka7JksYOBi8+n0+2j5iYGNn4QWkpLCwUYwg1Go3s2izpOWl8IdFcUalUUKlUMzrXycDAwKT5ToLHFw4MDIjf0KVLl2S/qeBrt2JiYib9nqRFGkMojS2U5rhMSEiYseMhullwfi661XF+LiIiIiIiIiIiIiIiooWBs94REREREREREREREREREREREREREREREREREREREREREREREREREREREREREdGc8ng86OjoQHd3t1ikG9739PSIdbvdDrfbLdtWuklpWloakpOTUVRUJG4OHHij4MD1qKioCB0p0dyQbn6dnZ0d9rajo6PiBsLSzbn7+vrQ29srnuvr60Nra6u4mffQ0JBsHzqdDhkZGUhNTYXBYBA3F87IyBA3F87OzobBYEBMTMxMHTbRdfP5fOju7kZHRwfsdruIRXa7HTabDXa7HT09Pejq6sLw8LBs25SUFPG9TklJQXFxsYg3wUtKSgr0en2EjpJo9sTHxyM1NRWpqalhb+v1emXxJXBxOByyuDOd32FmZqaIOVL8ycrKQlZWFpKTk2fqkIkWPJ/Ph56eHhE7pd+nw+FAT0+PiKPd3d3o7++XbatWq5GRkSHqcBkZGVi6dCkWLVqE1NTUkPU4hUIRoSMlmhtKpRJKpRLp6elhb+v3+2V1t+B1aTGbzXjnnXfgcDjgcDjg9/tlrx8YR9PT0yfV6QwGA7Kzs6FSqWby0IlmhMvlEjFJikdTrXs8Htm2KSkpon0xOTkZubm5qKioEHFIikWB6/Hx8RE6UqLI0Gg00Gg0yMrKuq7tpdgkxafAR2ndarXizJkzsNvtsNvtGB8fF9tHR0eLOqfBYEB6erqIV1K9z2AwIDMzEwaDge3/RBQxfr9f1I+lvk6pbiyVw6UyicPhwNjYmGx76VwXWC4pLy+X1ZGDF5bPaSGLiYlBUlISkpKSrmt7n88nyiPB5ZPAxWq14uzZs2LcwtV+uwaDQdQv0tPTRbklMzMTGRkZbOOieamvrw82mw2dnZ2ytl6pLt3T0wO73Q6HwyHbLjExUfT/B7bzpqSkICUlJWRfaWxsbISOkmh2REVF3VAsGhgYkPWHBi+9vb2iXXeq32HgmJysrCwRj6T23ZycHI7NoXmJ9SeiucX6E9HM8fv96O7uhs1mE3Grp6dH1J2kdSm2TUxMiG3j4uLENQ/p6elYtGgRli1bJhsfE6p/kv0etJDcaD1rbGwsZF9k8Diauro6UdYMrmsplcpJ/ZJS3EpLSxPPZ2dnQ6fTzcRhExHNmNjYWGzduhVbt25Fbm4unnjiCbz00ksoLy/H6OgoBgYGoFQqodFoptzHtdJJYxy7u7sn/S8xMXHK/QG46nmzqakp7Gvgli5dKhsHGai4uBgAYLVaJ/2vs7MTALBkyZJrvsb17Eej0eAHP/gBfvCDH8ief/PNNwEAq1atmrQvr9eLj370o8jKysKvfvWrabfnXbx4EX/+85/xX//1XzOa/nrzEygjIwMAYLfbAQAHDx7EwMAANm/eHDL9Y489hsceewzAle9DYWEhDh8+DADYtm3bpPR33HEHvvWtb+GNN97AQw89JJ4P9/2f7fThms/fn0DBny8RzW8LrYwAzFxZQDKdc224se7NN98MOzYCsx+LprJjxw68/PLLOHToUMjYHCxUrAgnts/2+3m97z8wO9+H6/l856qsGgrLAkREREREREREtFC5XC5YrVYx9l261iN47GB3d/ekefLUarUYhySNE1yyZEnIse/SGEKOT6KFTqfT3dDvwOVyhZzjJHhpbm4Wv93guZUTExNl492lcYTSuHhpLr3s7GxotdobPWSiWcP5uYhmF+fnIiIiIiIiIiIiIiIimn84CzIREREREREREREREREREREREREREREREREREREREREREREREREREREREREREc2Y0dFRdHZ2wmazoaurCxaLRaxLj62trZiYmBDbKJVKZGZmIiMjA5mZmbjtttuQlJQknpPWjUYj4uLiInh0RLcehUKBjIwMZGRkTHsbj8eDy5cvw+l0yn7bNpsNTqcTDQ0NeOutt+B0OtHd3Q2/3y+2TUpKgslkEr/34PWcnBzExnIafboxTqdTFocCY5HFYkF7ezu8Xq9Ir1QqZXGnrKwM27Ztkz2XmZmJ7Oxs3pye6AbFxsYiPT0d6enp095GijuBsSYw/vz973+H0+mE1WqFy+US2ykUCmRlZU0Zc6R1IroSO4PjZeB6R0cHxsfHRfrg2Jmbm4t169bJ6m8ZGRnIysqCXq+P4JER3XqioqKQkpKClJSUsLaTysiBcTRw/ezZs7DZbHA4HJPKyleLo4sXL4ZWq53pw6QFLLA+FyouNTc3o7+/X7ZNUlKSLAZVVlaGbFtknY5obiQnJyM5OTmsbdxud8j4JK1L9T6bzSY7B8TFxSElJUXWfhMcs9ivQETXY2xsDL29vVOWSWw2G9ra2jA8PCy2USgUWLRokawcsnjxYvZ5Es0TMTExSE1NRWpqaljbud3uKevSwf2iPT09snEQUl1lqrKK1EcbFRU104dLC5TH44HNZpuyrffSpUsYGBgQ6YNjV1ZWFtasWTOpnVdaJ6Ibo9PpoNPpUFhYOO1tgtvKAuNQW1sb3n33XdhstmmNzQn8Ozc3FzExMbNxmLQAsf5EdOth/YkWkquNlwkVw6TxMoExq7S0dFL8ysjIQHp6OstcRLMsPj4eBoMBBoMhrO2uNYbm4sWL6OrqCjlm7mqxKjMzE3l5eYiOjp7pQyUiwsmTJ7Fr1y709PSE/P+WLVvwxBNPwOl0ArhS99bpdBgYGMDg4CA0Gk3I7a6VTnq96Z5rFQoF9Ho9hoaG4Ha75+z6sC1btmDv3r2oqanB7t27Zf+rqakBANxxxx1zth8AqK6uBgDs2rVr0v8+97nPYXR0FC+//LLsPSosLMT+/ftRWVkZcp8/+clPUFVVhdLS0mnlYbrprzc/gWw2GwAgLS0NAPDggw/iwQcfnJRu//79+NSnPoW9e/fi0Ucflf0vsOw9laGhoWumAa7+/kcifbjmw/cnUPDnS0TzB8sIV8xkDAemd64NN9YVFhaGHRuvZrZjkUKhAABcvnx5WulDxYpwYvtsv5838v7PxvfhWkJ9vnNVVg2FZQEiIiIiIiIiIroVBc9VGTzPybXmNklKSsKyZctCXofFObeIIkOr1UKr1SI/P3/a24SaWzlwvbu7Gw0NDdMaUxhqbCHnV6bZwPm5iG5+nJ+LiIiIiIiIiIiIiIho5rF3noiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIpm1wcBAWi0W2tLe3o6OjA52dnejt7RVplUolsrKykJWVhZycHGzcuFGsZ2VlITMzE6mpqbyhNdFNRrpZfWZmJsrKyq6a1u12o6enB1arVZwnpPVz587h0KFD6O7uxsTEBAAgJiYGBoMBubm5yMrKQl5eHkwmE0wmE/Lz85Gbm8sbihNsNhuam5thsVjQ3NyMlpYWtLe3w2q1wmq1YmxsTKQ1GAzIzs5GdnY2SktL8YEPfADZ2dkwGo0wGo1IT0+HUqmM4NEQ0bUExp2Kioqrph0YGIDNZoPVakVnZyfa2trEueHQoUPo6OiAy+US6TUaDXJycmA0GpGTkwOTyYSCggLxqNPpZvvwiGbd+Pg42traRP2tpaUFLS0t6OzsRHt7O7q7u+H1egEA0dHRInZmZWWhtLQU27dvR1ZWFoxGI7KysmAwGKBSqSJ8VEQUrqSkJCQlJV0znc/ng91uR1dXF6xWK9rb29HZ2SnOGX/9619htVrh8XjENsnJyaK9JycnB/n5+aIeZzKZoNVqZ/PQ6Cbi9XphtVrR0tIiYlJbW5vsexb43UpKShIxSCoLSjHKaDQiLS0NaWlpETwiIpopKpVKxI1rGR4ehs1mQ1dXF9rb22Gz2dDZ2Slrb+zp6YHP5wNwpYybnp6O7OxsZGZmIi8vT8Sq/Px85OfnIzExcbYPkYjmGYfDgdbWVlFHbm1tRUdHB6xWK2w2GxwOh0gbGxuL9PR0USYpLS3FnXfeiczMTBiNRhgMBmRmZkKj0UTwiIhotqhUKqhUqmm1T4+OjsLhcKCzs1PWTi2VVU6dOgWr1YqRkRGxTUJCAoxGIzIyMmA0GkVZRXrMzs7meAoSBgYGRP+o9NjR0SHG7AT3f0h9H1lZWVi9ejVycnJEv2lmZib7QIhuAlK77rXG5ng8HtnYHOmxo6MDZ8+excGDB9Hd3Q2/3w8AiI+PR1ZWFrKzs5Gbm4u8vDwUFBSIftLMzExERUXNxSHSTYD1JyKaLtafaL4ZGRmR9UtKcUz6zgWOXQ41XkaKYTk5OcjIyEBGRgbUanWEj4qIZsJ0x9AAV8rDPT096OjoEOcPqSz8+uuvw2az4fLlyyK9QqGQXUOVm5sr65s0Go2MV0R0Xfx+P+x2O959911UVlZO+v/p06cBAOXl5eK5++67Dy+88AL+9Kc/4eMf/7gsfXl5OTZv3owf/ehHIt3rr7+O+++/X6QZHR3FW2+9BZVKhe3bt087r7t27cLzzz+PkydPYtOmTbL/PfHEE3jqqadgsVhm9Hy4adMmlJaW4qWXXsITTzwhrhHy+Xz43e9+B6PRiHvuuWfG99Pb24u0tDRYrVZkZmaK510uF5577jncf//9WLJkiew1vv3tb6Ourg5HjhyBQqGY9jG6XC68+OKL+MUvfjGj6cPJz3PPPYenn34aNTU1suf9fj/+93//FwCwY8eOaeUvlLVr1+K1117DW2+9hZ07d8r+d/ToUQCQff/Dff9nO3245tv3Z7Y/XyKaHSwjXDFTZQHg+s+1s2G2Y9FXvvIVdHd3Y//+/ZP+98YbbwAA1qxZI54LN1aEG9vno9n8PoT7+c52WZVlASIiIiIiIiIiupW43W4xblBa2traxPwEPT09GB8fF+mTk5Nl4wVXr14t5kvOyclBWloaUlNTeZ0F0S0onLmV/X4/HA4H7HY72tvbxfxc0pj4t99+G11dXbJ52WNjY2EwGMR1NtJ1XNJcJ/n5+UhISJjtw6SbCOfnIqKpcH4uIiIiIiIiIiIiIiKiq+OMPkREREREREREREREREREREREREREREREREREREREREREREREREREREREREREJPh8PlitVlgsFrFINw62WCxwOBwAgKioKGRmZsJkMiEvLw/Lli1DVlYWcnJyxM2CeZNgIlKpVMjLy0NeXt6UacbHx9Hd3Y329nZYrVZ0dnaK9aNHj+K5556D0+kEAMTExCA7O1vctDh4SUlJmaMjo9k0OjqK1tZWNDc3w2KxTHp0u90AAKVSCZPJhIKCAixbtgx33XUXjEajLBYpFIoIHw0RzSWdTgedToeSkpIp07hcLnR0dKC9vV3cyL69vR2XLl3Cm2++CavVKm5mn5ycjIKCAnGuCXzMzs5GVFTUXB0a0VX19vaGrL9ZLBZ0dHSI77Rer4fJZEJ+fj7WrFmDXbt2ISsrC0ajEUajERkZGYiLi4vw0RBRJMXExCAjIwMZGRlYtWrVlOnsdjs6OzthtVrR0dEhYuqFCxfw2muvobOzE36/HwCQkpISsv6Wn58Po9GImJiYuTo8mgN2ux0tLS1ikWJTS0sLOjo6MD4+DgBQq9XIz88X7Yrbt29Hdna2qMsZjUYkJCRE+GiIaD5KTEzE4sWLsXjx4inTeL1e9PT0iHqfFKdsNhv+9re/4fe//z26u7tF+vT0dOTn58uWwFgVG8vbfhLdbFwuF1pbW2XlkpaWFvHc0NAQgCvl36ysLOTl5SE3NxfLli1DZmYmjEYjMjMzkZ2djfT0dJZZiWhaFAqFqNdcjdPpFHVqm80m+ketVitqamrQ0tIi+sLi4uJgNBqRl5cnyimB6xkZGXNxaDRH/H4/Ojs7p+wj7e3tBQBER0fDaDTCZDIhJycHa9asEd89qZ9Up9NF+GiIaC4plUrk5uYiNzd3yjRjY2Mi3rS1tYn409bWhpdffnnKsRjBj/n5+RyHcYth/YmIIoH1J5oJXq8XHR0dU/ZN9vT0iLRSP0BeXh42btwIo9GIrKwsZGZmIicnBwaDgX0BRBRSamoqUlNTsXTp0inTuN1utLe3i1glxa329nYcOnQIFotFlKtjY2NFu06ovkleg0VE1/Kxj30M3//+97F582YkJyejq6sLL730Eh5//HFUVFRg9+7dIu33vvc9HD9+HA8//DDUajWqqqowMDCA//iP/0BXVxcefvhhWbqHHnoIarUamzZtQldXFx555BF0dXXhZz/7GdLT06edR2l/n/nMZ/Dkk0/i9ttvh8/nw4EDB/D444/jl7/8pazstXXrVpw7dw6vv/46Kisrr+t9iY6Oxi9+8Qts2bIFn/70p/GjH/0IcXFx+MY3voGmpiYcOnQISqVSpG9tbUVBQQEmJiZQU1MjxkqGux/gStv+pz/9aTz11FMwGo04d+4cHnroIaSnp+Opp56SpX3hhRfwne98BwCg0WjCOsbnn38earUa991334ylv578nDlzBg8++CAefvhhZGdno62tDXv37kVNTQ0+//nPY+3atdPaTyj/8i//ghdeeAFPP/00CgoKcP/99yMmJgavvPIKvv/97yMrKwtf+cpXZNuE8/7PRfpwzbfvz2x+vkQ0uxZ6GWGmygI3cq6dLbMdi377299iyZIl2L17NzIyMtDV1YWnn34a+/fvR0VFBfbs2SNLH06suJ7YPp/MxfchnM93tsuqAMsCRERERERERER08xgbG0N7e7tsHHzgeuD4weTkZOTn5yM3Nxdr1qzBhz/84Unj4IPb1oiIQomKikJaWhrS0tKuOqbQ4/Ggs7NTjCPs6uoS4+BPnDiBF198UVwrClwZ5zzVGPicnBzEx8fPxeHRHOL8XEQ02zg/FxERERERERERERERLURRfukuWUREREREREREREREREREREREREREREREREREREREREREREREREREREREREREtGB4PB40NjaioaEB9fX14rG5uRljY2MArtzs02QyyZbAm3PyZvdENFecTicsFkvIpb29HV6vFwCg1WpRXFyM0tJSlJSUiMf8/HxER0dH+Cgo2ODgIBobG1FXV4f6+nrU1dWhoaEBbW1tmJiYAACkpKSgoKAABQUFMJlMssfMzExERUVF+CiI6FYzNjaG1tZWWCwWNDc3o7m5WaxbLBaMjIwAABQKBYqKilBSUoKlS5eipKQEZWVlKCws5A3saVZMTEygtbVVVoerq6uD2WzGwMAAACA2NhZGo3FSPU5aFi1aFOGjIKKFYnR0FC0tLVPW44aHhwEAcXFxKCgoQFlZGYqLi8VjcXExVCpVhI+CpjI+Po5Lly6hvr4eZrMZ9fX1aGxshNlsxtDQEIArMSknJwf5+flikdoU8/PzkZaWFuGjIKKFbmRkBC0tLSJeBa8Hns/y8vJQWloqYpS0rtPpInwURAubz+dDS0sLGhsbRVmkoaEBZrMZvb29Ip3BYEB+fj7y8vJkj/n5+TAajYiPj4/gURARhdbd3Y2Wlha0traKcoq03t7ejvHxcQCAUqnEkiVLUFRUhOLiYpSUlKCoqAhFRUVITEyM8FHQVLxeL5qbm3HhwgXRzivFMI/HAwBQqVQh+0cLCgqQl5fH+EVEs8Jms4XsH21ubobD4QAAREdHIycnZ1IfaUlJCTQaTYSPgKbC+hMR3cpYf7r1DQwMyPokGxoa0NDQgJaWFjF2WLrmIVS/pMlkQkJCQoSPgogWOofDMWXfZEdHh4hXarVaxCepT7KkpASFhYULujy+fPly7Ny5E/v27Yt0VogiZmJiAn/961/xyiuv4J133kFHRwfsdjtUKhWKioqwa9cufPGLX5xU7unr68O+ffvw6quvwmq1IiUlBZs2bcLjjz+OxYsXT5kuISEBlZWV+NrXvoatW7cCAN59912sW7duUt78fv+k5y5fvozvfve7eOWVV9DR0QG9Xo/y8nJ89atfxbZt22Rpq6qqcOHCBbz++ushhP1BIwAAIABJREFU9x+Os2fP4hvf+AZOnjyJiYkJ3HbbbXj88cexfv16WbrW1lYUFBTA7/fj3LlzWLZs2XXtBwCOHDmCn/70p/jb3/6GgYEBFBQU4OMf/zi+/OUvTxoDee+99+L111+/6jH89a9/RWVlpew5v9+PJUuW4IEHHsB3vvOda74P000fbn5GR0dx8OBB/Pa3v8V7770Hq9UKpVKJ8vJyfPazn8U//MM/TLmff/qnf8LPfvazSc9v374dhw8fFn87nU5873vfw8GDB9HS0gK/3w+j0Yi7774bjz76KNLT02Xbh/P+z0X6Q4cOYceOHSHfg2effRZ79uy57v3P9vfnRj5fooXCbDajuLgYtbW1WLFixbzYP8sIcjdaFrjecy0w/VgXbvrZjEUulwsHDhzAgQMHYDabYbPZEB8fj6KiInzkIx/BF7/4RdlrXE+sCDe2h/v+zGb6ufg+hPv5ArNXVp2tssDevXuxf/9+mM3m69qeiIiIiIiIiIgWNrvdLsa9S+MHzWYz2tvb4fP5AAAajUY27j14HDyvcyCi+WhwcFA2/j14PPzg4CCA96/jkua7LC4uFutT9bPQ/MD5uYjoVsD5uYiIiIiIiIiIiIiI6Cbyqyh/qCtbiYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIjoljA0NISGhgbU1dWhoaEBDQ0NqK+vR0tLCyYmJhAXF4fCwkJxU82SkhKYTCaYTCbeEJqIbgperxft7e2wWCxobm4W57mGhgZYrVYAgEqlEjcRLisrEzcTXrx4MWJjYyN8BLe+wcFBNDQ04MKFC7LHtrY2AIBSqURJSQlKSkpQVlaGoqIimEwmFBQUQKvVRjj3RERyXV1daG5uxqVLl9DY2Ij6+npZ+To+Ph5LlixBaWkpysrKUFpaitLSUixZsoQxh6bF6/Xi0qVLqK+vR2Njo6jLNTY2wu12AwCys7NF7CwpKUFBQQFMJhNycnIQFxcX4SMgIrq2np4eWCwWWCwWNDY2ijarpqYmjI+PIzo6Gnl5eSKOSnW5kpISaDSaSGd/wRgaGpJ9PlLZx2KxiM8pNzdX1LGLiopQUFCA/Px8GI1Gln2I6KbmcDhgsVjQ0tKCixcvivOg2WwW5fLMzEyUlJSgqKhIxKvS0lJkZGREOPdEt5bh4WE0NjaKconZbEZjYyOampowOjoKAMjKykJxcTGKiopEX2deXh7y8/OhUqkifARERDPL5/Ohs7MTLS0taGlpgdlshtlsRkNDA5qbmzE+Po6oqCjk5OSgqKhI9JFKZRaDwRDpQ1gwvF4vmpqaRBtvXV0d6uvrYTabMTY2Jmv/kPqwCwsLYTKZkJmZGensExHJuFwuMS7n4sWL4pwW2H8ltRUuXbpUjP8oLS3luI85xPoTEZEc6083l66uLlmfpBTPbDYbgCvjTANjWFFREfLz85Gfn4+0tLQI556I6Pp5vV5YrVYxjsZsNovzYGtrKyYmJhAbGwuTySTG2QeOHVSr1ZE+hFm3fPly7Ny5E/v27Yt0VoiIiIhoATObzSguLkZtbS1WrFhx0+2fiCgS9u7di/3798NsNkc6K0RERERERERENE/5fD5YLBbZtfzSutPpBABotVoxzrOkpASFhYXIz89HXl4eUlJSInwEREQzr6+vDy0tLWhtbUVTU5O4VshsNmNgYAAAkJSUJBtXLZ0jCwoKEBMTE+EjWDg4PxcRLWScn4uIiIiIiIiIiIiIiOaRX0X5/X5/pHNBREREREREREREREREREREREREREREREREREREREREREREREREREREREREREQ3bmhoCLW1taipqRFLY2MjJiYmEBcXh8WLF6OsrAwmkwmlpaUoKytDaWkpVCpVpLNORDQrXC4XmpqaYLFYUFdXh/r6etTV1cFsNsPn84lzY0VFhVjWrFkDhUIR6azftMbHx/Hee++hurp6UiyKj49HYWGhiD/SY3FxMWJiYiKddSKiGzI2NoampiYRa64VczZs2ICVK1fy/LfA+Xw+NDY2yupwZ8+excjICAAgIyNDFjdNJhNWrFiB1NTUCOeciGh2eL1etLe3i1gq1eVqa2sxPDwM4Mq5MbAOd/vttyM5OTnCOb/5uVwuvPfee7KYFFiOMRqNsnhUWlqK8vJyJCYmRjrrRERzzmazyeJUfX09Lly4gO7ubgCAXq9HWVmZLF6VlpYiKioqwjknmv8GBgZw/vz5sMoky5cvh1arjXTWiYjmBaleHVhOsVgsOH/+PHp6egCELquUlJQgOjo6wrm/+dlsNpw8eVL0k16trbe0tBQrV66EWq2OcK6JiG6cVE8O7CM9d+4choaGAExu092wYQOSkpIinOubH+tPREQ3hvWnyLLZbLIYVlNTg66uLgBX3veCgoJJcYzjTIloIRobG4PVapXVt+rr69HQ0CBrdwqMVZWVlbfc+MLly5dj586d2LdvX6SzQkREREQLmNlsRnFxMWpra7FixYqbbv9ERJGwd+9e7N+/H2azOdJZISIiIiIiIiKieWB8fBwXL16UjR0MnE8mKSlp0tjBsrIy5Ofn8zp9IqL/z+l0Tpqbq76+Hq2trbL55wPHFa5atQoJCQmRzvpNj/NzERFNH+fnIiIiIiIiIiIiIiKiOfarKL/f7490LoiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIKDyDg4M4c+aM7MbBTU1NmJiYQGpqquzml8uXL0deXh5iYmIinW0ionnB7XajsbERtbW14hx67tw5uN1uqFQqLF++XHYeXbp0Kc+hIYyOjqK2thanT5/G6dOnUVNTg/r6evh8PiQnJ2P16tVYvXq1iEX5+fmIjo6OdLaJiOaUx+NBQ0ODOF9KMcfj8UCtVqO8vFycL1evXo0lS5ZEOss0S3w+Hy5cuCCrx01V/li5ciVKSkqgUqkinW0ionlhYmICLS0tOH/+vKwtzG63Izo6GoWFhbI6XEVFBTQaTaSzPW85HA7U1NTg7NmzOHPmDM6cOQOLxQIAMBgMWLVqFVatWoXy8nJRl2OdmIjo2ux2O+rq6sT59ezZszCbzfD5fEhKShLnVuk8u2TJEkRFRUU620QR09HRIX4r0tLR0QEAyMrKQnl5OcrLy7Fy5UosW7YM+fn5iI2NjXCuiYhuXj09Pairq8O5c+fEebexsRFerxdarRYrVqwQ597y8nKUlZXxvHsVTU1Noo/09OnTOHPmDIaGhqBQKLBixQpUVFRg9erVKC8vR3FxMdt6iWjBmZiYQGtrK9577z3U1NSI82Vvby9iYmJQUlIizpXS+VKhUEQ62/MW609ERHOL9aeZ4/f70dTUJPokpcXpdCImJgZLliwRbefSe5menh7pbBMRzXs+n0+Mowkc+9HV1QUAyM/PF32Sq1atQkVFBVJTUyOc6+u3fPly7Ny5E/v27Yt0VoiIiIhoATObzSguLkZtbS1WrFhx0+2fiCgS9u7di/3798NsNkc6K0RERERERERENMek+ZJra2vFWMz6+np4vV6o1WrZWMwVK1agqKgIarU60tkmIrppDQ8Pw2w2y8bAnzt3DoODg4iNjUVJSYlsDHx5eTm0Wm2ksz1vcX4uIqLZwfm5iIiIiIiIiIiIiIholvwqyu/3+yOdCyIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiurqRkRH85S9/QXV1NU6ePIkTJ05gbGwMer0eZWVlqKioEEtpaSlvbElEFCafz4fGxkbU1NSI5ezZsxgZGUFiYiLWrVuHbdu2Yf369Vi7di3i4uIineU55/P5UFtbiyNHjqC6uhonTpyAy+WCVqvFsmXLGIuIiKbJ6/XCbDbLYk5NTQ08Hg/S0tKwadMmrF+/Hhs2bEBFRUWks0vXaWJiAg0NDTh58iSOHDmCI0eOwOl0Ii4uDosXL5bFzTVr1kChUEQ6y0RENx2bzSaLpadPn0Z3dzdiYmKwcuVKEU/vvPNO6PX6SGc3Yux2O06dOiVi0pkzZ+D3+5GRkSGLR2VlZTCZTJHOLhHRLWVsbAxNTU0h63+pqalYu3YtNmzYgG3btqG8vBzR0dGRzjLRrLFYLKKf88iRI7BYLAAwqUyyZs0aGAyGCOeWiGhhGB8fx8WLF6/aPyrVrTds2AClUhnpLEeMzWYTMezw4cNob29HbGwslixZIotjq1evXtDvExHRtQS36Z46dQoOhwMqlQqrVq0SdeSqqirEx8dHOrsRw/oTEdH8w/rT9FksFjHG9O2334bVag1ZfyovL0diYmKks0tEdEtxOp2oq6uTxauGhgb4/X6YTCYRq+666y7k5OREOrvTtnz5cuzcuRP79u2LdFaIiIiIaAEzm80oLi5GbW0tVqxYcdPtn4goEvbu3Yv9+/fDbDZHOitERERERERERDTLBgcHcerUKTF+8O9///uU8yWXlJTwmnoiojlytfm5ioqKxLVcW7ZsQUpKSqSzGzGcn4uIKHI4PxcREREREREREREREc2AX0X5/X5/pHNBRERERERERERERERERERERERERERERERERERERERERERERERERERERERERERyIyMjOHnyJI4dO4Zjx47h73//O8bHx1FcXIzNmzdj8+bNuP3222E0GiOdVSKiW5bX60V9fT1OnDiBY8eO4fjx4+jt7YVer0dVVRW2bNmCzZs3Y/ny5bfkDYQnJiZw7tw5vP322zh69ChOnDiBwcFBZGZmYuvWrdiyZQs2btyIwsJCREVFRTq7REQ3tdHRUdTW1uLYsWN4++23UV1djeHhYeTk5GDLli3ivMvy//w1MTGB8+fPi8/wxIkTcDqdSElJQVVVFTZv3oxNmzahtLQUsbGxkc4uEdEty2q14i9/+YtoU2toaEBsbCzWrFkj2tTWr1+PxMTESGd11thsNhw/flwsjY2NiI2NRUVFBaqqqrBp0yasXbsWKSkpkc4qEdGCJNX/qqurcezYMVRXV6O/vx/JycnYuHGjqDvcqm2OtDD4/X7U1dXh2LFjeOedd3DixAl0d3cjMTERlZWVqKqqQlVVFVatWgWtVhvp7BIRUQCv14uGhgZUV1fjnXfewfHjx2Gz2aBSqXDbbbdh06ZNqKqqQmVl5S1dt+7s7MTRo0dFP2lbWxsSEhKwYcMG0UdcXl4OhUIR6awSEd30Ll26hOrqahw9ehRHjx5FZ2cn1Gq1GJezZcsWlJeX37J1ZNafiIhuXqw/vT9W5vjx4yKW9fb2QqfTYePGjdi0aRM2btyIFStWQKlURjq7REQLUl9fH06dOoUTJ07g+PHjOH36NLxeL4qKikSs2rx5M7KysiKd1SktX74cO3fuxL59+yKdFSIiIiJawMxmM4qLi1FbW4sVK1bcdPsnIoqEvXv3Yv/+/TCbzZHOChERERERERERzbDu7m6cOHFCjEmpq6tDVFQUli5dKsakrF27lvOlERHNQ1arFe+++64YA3/+/Hn4/X6UlpaiqqpKjAPPzMyMdFZnDefnIiKa3zg/FxERERERERERERERhelXUX6/3x/pXBAREREREREREREREREREREREREREREREREREREREREREREREREREREREREREWC323H48GEcOHAA//d//4fR0VGYTCasX78eGzZswF133YWcnJxIZ5OIaEGzWCw4cuQIjhw5grfffhu9vb1ITk7G1q1bce+99+K+++6DRqOJdDavm8fjQXV1NQ4ePIgDBw6gq6sLKSkpqKysxIYNG7Bt2zasWrUKUVFRkc4qEdEtzev14ty5cyLmVFdXw+PxwGQy4d5778VHP/pRrF+/nufjCHO73Th58iQOHjyIl156CTabDRqNBmvXrsW2bduwbds2lJeXIzo6OtJZJSJasOx2O44fP47q6mqcPHkSZ86cQXR0NCorK/HRj34Uu3btgtFojHQ2b8jExATOnj2LgwcP4tChQzhz5gxiYmKwYsUK0a545513Qq/XRzqrREQUwsTEBBoaGnDy5Mkp2xw/9KEPQafTRTqrRFc1PDyMo0eP4tChQzh06BBsNhvUajUqKytFmWTjxo1QKBSRzioREYXJZrOJskp1dTXq6+sRExODyspK7NixAzt27EBpaWmks3nD6urqcOjQIRw8eBB/+ctfRN1aautlHCMimhvSuJzq6mocPXoUnZ2dWLRoEe655x7s2LEDd999N9RqdaSzeUNYfyIiunUthPrTyMgI3nrrLVF/6urqko2VWb9+PdauXYu4uLhIZ5WIiEIYGRnBmTNnRLx65513xLVr9957L3bs2IFNmzbNq/P48uXLsXPnTuzbty/SWSEiIiKiBcxsNqO4uBi1tbVYsWLFTbd/IqJI2Lt3L/bv3w+z2RzprBARERERERER0Q3y+Xyora2VzWsSHR2NoqIiMT/l1q1bkZycHOmsEhFRmAYHB3Hq1CkxP9eJEycwNjY2r8cVhovzcxER3dw4PxcREREREREREREREV3Dr3h3QCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKiCHI4HHjyySexbt06GAwGPPjgg1AoFPjZz34Gq9WK5uZmvPjii/jsZz+LnJycSGd3Sj6fD8888wxuv/126HQ6xMXFITMzEx/84Afx5JNPorW1NdJZjLja2lrcc8890Ov10Gg02LZtG06ePHld+/rTn/6EJUuWIDY29qrpvF4vfvGLX+C2225DcnIykpKSUFFRgSeffBJjY2OT0vt8Pvz3f/83Vq5ciYSEBOh0OmzduhVHjhyZlNbpdOKZZ57B1q1bsWjRIqhUKixevBif+MQncO7cuZD5CWf/kvHxcfzoRz9CRUUFNBoN0tL+H3v3HR9Ftf4P/LPpEDBAKAkBAcEYpUS6QriAIAEhNCl6AREVUBBERfEqF71gQcXrVVEB6c2GP9Ao0hGpX+k9NFFawNADJECS5/cHr6zZbMme3Tk7m+Tzfr34g5mTM8/O7J7nnDNndiuiQ4cOSE5OhojYlVc5z57Ek1fnzp1hsVjw5ptven1+RATr16/H0KFDERsbi9DQUFSsWBEJCQmYO3euw9eqEo9qeU/jUb1e3sRvljvuuAODBg3CN998gzNnzmDHjh146aWXcOLECQwYMACVKlVCr1698P333zv8nPmj7OxsLF26FP369UOFChXQrl07bNq0CcOGDcOOHTtw5swZJCcnY9SoUWjYsCEsFovZIdthHvqbt/lGtf2aNGkSLBaLy38dOnSw+zuV9kI1n6meh4SEBKexjxgxwqvz44t8qRK/J+VzudP/UH0/eJJvVN4PRuRXswQFBaFhw4YYNWoUli9fjvPnz2P58uXo1KkTFi1ahBYtWqB69ep49dVXsX//frPDLVZu3LiBH374Ab1790ZkZCQSExPx22+/YdiwYdi+fTsuXryI5cuXW/NmQIB//kQQc2fBfD2G0z0mUK3fk3hUcoxq/Z72Odw9P6rlfdEH0j2G9qRP4M2YzywVK1ZEz5498dFHH2HLli04ceIEpk+fjujoaIwePRrVqlXDfffdh08++QR//fWX2eG6Lfcz9OyzzyI6OhqNGjXC3LlzkZCQgCVLluDixYvYsmULPvroI/Ts2RNlypQxO2SnmJP+ZlTuUa3H3c+2J22falujc75O9fUCvhl/qLatOtt6T8eLudzNtbr6Rt7Gb5aAgADUrl3bZs5x586dGDVqFM6cOYMnnngCFStWRFJSEubPn4+rV6+aHTKR1aVLlzBz5ky0b98eZcuWRbdu3bB//34MHz4c27Zts46R33jjDbRt2xahoaFmh+wU+yQF8/e+iu7yvpgbVp0L96e5YdX6/fH8qPRFzbi3a6bKlSujZ8+emDx5Mvbu3Yvjx49j+vTpqFKlCsaPH4/atWujdu3aGDt2LA4ePGh2uEpSUlIwevRo1KhRA3Xq1MGnn36K+vXrY8mSJbh06RK2bNmC8ePH+3UeYw77m7e5yhfjOt3rcvIzepycy93c4Ivy7l53T+d0ddXvi3kWT+6z+4PcdTmzZ8/G8ePHsWvXLrz88ss4dOgQHnnkEURFRaFPnz74+eefkZ2dbXa4buP4qXgxY62oSnlf3OcC9LbRqm2cJ22izrWQvm7TC4pHpX7da6EK81ofoOiOn65du4Yvv/wSnTt3tsljI0aMwI4dO2zWyiQkJCA4ONjskB1iDvubr+f6VNt63W2NL9ag6G77VHKJ7nWd/rgGyF/vk/qDkiVLIiEhwW5tapcuXbB48WI8+OCDiImJwdChQ7Fu3Tq/z72u6Gz3J0yYYH2PVKlSxbigFZQqVcru/TphwoQC9+WqUqWKXZnRo0c7PV5UVJRd+ZSUFJsyPXr0gMViwcmTJ222jx492ubvxo8f79ZrNPI8u3NOfB2Tv/jqq6+sryksLKzA8mfPnrU5j/Xr10dmZmaB5SwWCxo1aqQcn6NrlzfeevXq4dNPPy3U7VVxpqO9Ivfbqi1btuDxxx9H9erVERYWhjJlyqBx48YYO3YsLl686MOIi47Nmzfj8ccfR40aNVCiRAmUK1cOderUwcMPP4zPP/8cR44cMTvEQsHo+SEj52uLkqLeX86Ln03/cOjQIVgsFtx3331mh2IaI9dZF4RtHxERERERERERGSE7OxurVq3CoEGDUKlSJZvvNfn5559x8eJF7N27F5MnT0bPnj0RGRlpdshOcf1gwcxaA69yXN3PmAL+80yz6j0z3c/s6n7uzh/XqBendYW5n7033ngDy5cvx7lz57B06VJ07tzZuq4wKioKTz31FFasWFFonufi93MVTb7+vmXV4/pbe+bJum2d6/J98R0TgHfvE7OfE9D9DJm35c3C7+ciIiIiIiIiIiIiIqICCRERERERERERERERERERERERERERERERERERERERERERERERERERERERERER+VROTo4sW7ZMOnfuLEFBQVKqVCnp16+f/PTTT5KRkWF2eB559NFHJSAgQN599105fvy4ZGRkyOHDh+XVV18Vi8UikZGRZodoqk2bNkmJEiWkd+/ecurUKUlLS5OBAwdKUFCQLF261O16Dh8+LElJSVKvXj257bbbJDAw0GX5vn37CgD517/+JWfOnJGzZ8/Ku+++KwCkU6dONmWzsrKkU6dOEhwcLJ988omcPXtWfv/9dxkwYIBYLBb58ssvbco/+eSTEhQUJP/73/8kNTVVrl69Kr/++qvcc889EhgYKAsXLvSqfhGRK1euSEJCgtSrV0/WrFkj165dkz///FN69OghAGT37t0en2dP4slr1qxZAkAAyLhx4+z2q56f/fv3CwBp27at7Ny5UzIyMuTIkSPy6KOPCgB58cUXvYpHtbwn8aheL2/i91enT5+WKVOmSKtWrSQgIEDKlSsnI0aMkEOHDpkdmkOpqakyZswYiY6OFgBy3333ycSJE+XkyZNmh6aMeegWI/KNavv1+eefWz+/zv6NHTvW5m9U2wuVfObJeWjevLnT2J977jmvzo8v8qVK/J6UV+l/qL4fPMk3Ku8Hb/OrP9u6dau88sorUrVqVQEgjRs3llmzZklmZqbZoRVZhw8flhdeeEEiIyMlICBAWrZsKZMnT5bTp0+bHZpHmDtdM2MMp3tMoFq/J/Go5BjV+j3pc6icH9XyuvtAvhhDq/YJvBnz+avMzExZvHixPPbYY1K6dGkJCgqSpKQkWbp0qeTk5JgdnkOnT5+WN998U6pXry4ApHbt2vLWW2/J3r17zQ7NY8xJtxiVe1TrUflse9L2qbQ1uufrVF+viP7xh2o8utt61fKq519338ib+P3Z2bNnZfbs2fLQQw9JcHCwhIeHS//+/eW3334zOzQqpnJycmTlypXSs2dPCQ0NldDQUOncubPMnTtXzp07Z3Z4HmOfxLXC0FfRXV733LCI+ly4P80Nq9bvb+dHtS/q63u7/uzmzZuyevVqGTZsmERFRQkAadiwoUyePFnS09PNDs+h69evy5w5c6Rp06YCQKpUqSIvv/yybNmyxW/nA1xhDrvFiFyle1yne11OfjrGyaq5QXd5levuybyGzvp1z7OIqOfOwuDUqVPy2WefSbNmzcRisUhUVJSMHj3ab9e5cPxUPJm1VlTn2g4RvWs5PYlHtY1TLa97LaQv23R34lGpX/daqKK81qcwjp82b94sAwYMkFKlSklQUJA89NBDMmvWLDl79qzZoXmEOewWM+b6VNt63W2N7jUovmj7VHKJ7nWd/rgGyN/ukxYm+/btk7ffflvq1KkjAKRatWoybtw4SU1NNS2munXrymuvvab8d75o9+Pj4yUmJsZmW3p6utSqVUs6duzodf0F2b59uwCQLl26KO3LdfToUQEgzZs3d+t4U6ZMEQAyYsQIu31ZWVlSpkwZASDTpk2z23/u3DkpU6aM3Lhxw61j5eXoPHvCnXPi65j8SZs2bSQ0NNTt8ps3b7a2f4MHD3ZabuPGjV5/3hxdu+vXr8v27dutbfhLL73k1THIPN60V75sc43g63hdtVWvvPKKBAYGyogRI2T37t2SkZEh58+fl+TkZLn33nulQoUKsm7dOp/EWRRkZ2fLyJEjJSgoSF566SXZv3+/ZGZmyunTp2XZsmXStm1ba5t58+ZNs8P1e0bODxk1BhYRSUlJEQCyY8cO1ZfkF/XnVxz6y/xs+pd//etf1vNdmJ998JTR66xdMbLt89bYsWMlNjbWp8ckIiIiIiIiIiLv/f777zJq1CjrWsf69evL+PHjJSUlxezQPMb1g66ZtQZe9bi6n8H1p2eaVe+Z6X5mV/dzd/64Rp3rCv924MABee+996RBgwYCQCpVqiQvv/yyHDlyxOzQHOL3cxVdZnzfsupx/a09U123rXtdvi++Y8Kb94k/PCeg+xlZAQ6VAAAgAElEQVQyb8r7M34/FxERERERERERERFRsTcTZkdARERERERERERERERERERERERERERERERERERERERERERERERERERERERERFRcZGVlycyZMyUuLk4ASKtWrWTevHly9epVs0Pzym+//SYAZODAgQ73DxkypNj8mLAj2dnZUrt2bYmOjpZr165Zt2dlZcldd90lVatWlczMTLfqevTRR+Wdd96RmzdvSkxMjAQGBjote+TIEQEg9evXt9v34IMPCgCbHzGdOXOmAJBhw4bZlM3JyZG4uDgpW7asXLhwwbr9ySeflEGDBtnVvWPHDgEgd955p8121fpFRJ555hm57bbb5PTp0zbbr1y5IqGhoTY/+q56nj2JJ9fJkyelbNmy0q9fP6c/cqt6fvbv3y9BQUFy/vx5m+3Xr1+XyMhICQ0Ndfo+cSce1fKexKNyvbyJv7A4duyYvPPOO1K9enUJCAiQTp06yebNm80OS0REfv/9d+nfv7+EhIRIhQoV5NVXX5WDBw+aHZbHmIduMSrfqLZfn3/+uXTp0sVhXQcPHpTQ0FBJTU212a7SXqjmM0/OQ/Pmzd3+fKqeH1/kS5X4PSmv0v9QfT+o5hvV94M3+bWwyM7OlpUrV8ojjzwiwcHBEhUVJe+8806hH2P4ky1btkhSUpIEBARItWrV5K233pI///zT7LC8wtzpmlljON1jAtX6PYlHJceo1u9JnyOXjjGT7j6Q7jG0iHqfwNMxX2Fx7do1mT9/vrRu3VoASFxcnMyYMUOysrLMDk1ERFJSUqRv374SEhIikZGRMnLkSNm5c6fZYXmNOekWo3KPJ/WofLY9aftU2hrd83UiesejvphP093Wq5bP5e7519038jT+wiQtLU0+/fRTiY+PFwDSuHFj+X//7/9JTk6O2aFRMXDjxg354osvrPc5mzVrJtOnT3faNhcm7JO4Vlj6KrrL654bVu17iPjX3LBq/f52flT7or68t1uYZGVlyYoVK+Sxxx6TsLAwiYiIkBEjRsipU6fMDk1Ebo393333XYmKipLg4GDp3bu3LF++XLKzs80OzWPMYbcYlat0j+t0r8vJS8c4WUQ9N+gsr3rdVec1dNeve57Fk9xZ2Bw6dEhGjx4tFStWlJCQEOnXr58cPnzY7LBEhOOn4pB7nDHrPqNqed33uXS3oaptnCdtou61kL5q092JR7V+3WuhisNaHxH/Hj/l5OTIokWLpEmTJgJA6tWrJxMnTpS0tDSzQ/MKc9gtZs31qbb1utsa3WtQfNH2qeQS3es6/XENkL/dJy2sdu3aJS+99JKUL19eQkJC5J///Kfs37/f53HUrVtXXnvtNaW/8VW7Hx8fLzExMTbbLl++LHfccYd06NDBrnx4eLg0b97c6+Pm2r59uwBw+JlytS/X0aNHBYDbMR07dsy6riq/9evXS8mSJQWA9OzZ027/V1995TIWVxydZ0+4c058HZM/adOmjYSGhrpdfvPmzRIaGiqRkZECQObPn++w3MaNG73+vLm6dseOHZOAgAAJCgqSc+fOeXUcMoc37ZWrNtddRrfNrhgRrwpnbdW4ceMEgEyaNMnh3129elUaNmwopUuX9jr3+/L8munVV18VADJlyhSH+7OysqRDhw4CQG7evOnj6NzjT9fKqPkhI+drRW6t5QYgO3bscP/FKNBdf17Fpb/sb59Nf/qc+Vp2drbExMRI/fr1BYCMHDnS7JB8Ssc6a2eMbvu8NXbsWImNjfXZ8YiIiIiIiIiIyDsbNmyQjh07SkBAgMTExMiYMWMkJSXF7LC8xvWDrpm1Bl71uL54BtefnmlWvWem+5ld3c/d+eMada4rdOzAgQPyxhtvSJUqVSQgIEA6dOgg69atMzssEeH3cxV1Zn3fsupx/a09U123rXtdvu585c37xB+eE9D9DJk35QsTfj8XEREREREREREREVGxNDMARERERERERERERERERERERERERERERERERERERERERERERERERERERERERESk3dKlSxEfH4+nnnoKzZo1w65du7B69Wr885//RMmSJc0Ozyt79+4FANx1110O9/fq1cuX4fidX3/9FXv37kWPHj1QokQJ6/bAwEA8+uijOH78OH788Ue36po2bRpeeeUVBAUFFVj2+PHjAIC7777bbl9cXBwA4NixY9ZtCxcuBAAkJSXZlLVYLOjSpQsuXLiABQsWWLdPnToVkydPtqs7Pj4eJUqUwJEjRyAiHtd/5swZTJkyBX369EGlSpVs/iY8PByZmZmoU6eOdZvqeVaNJ6+BAweiZ8+eaNeuncP9gPr5iYuLw82bN1G2bFmb8iEhIahatSquX7+OzMxMj+NRLa8aj+r18ib+wqJq1ap45ZVXcPjwYXz33Xc4d+4cmjRpgh49euCPP/4wJab09HQ8//zziIuLw8aNG/H555/j2LFjeOutt3DnnXeaEpMRmIduMSrfqLZftWrVQosWLRzW9cknn6Br166IioqyblNtL1TzmZF51xHV86M7X/qCSv9D9f2gmm9U3w/e5NfCIiAgAA888AC+/PJLHD16FAMGDMBbb72F2NhYzJw50+zwCrVjx46hV69eaNy4MdLS0rBgwQIcOXIEr776Km6//Xazw/MKc6drZo3hdI8JVOvX3Yaq1q+aY/LSMWbS3QfSPYZWpbt+f1CiRAk8+uijWLVqFXbv3o3mzZtj4MCBqFu3LhYvXmxaXGfOnMETTzyBOnXqYNu2bZg0aRKOHz+O999/H/Xq1TMtLqMwJ91iVO5RrUf1s+1NW+wO3fN1usejuufT/LktdjfX+qpvVJSVL18eQ4YMwY4dO7B27VrcfvvtePjhh9GkSROsWbPG7PCoCPvyyy8RFxeHoUOHokWLFti5cyfWr1+PAQMGoEyZMmaH5zX2SVwrLH0V3eV1zw2r9j18QSV3q/K386PaF/Xlvd3CJDAwEG3atMGsWbNw4sQJjB49Gt9++y1q1qyJkSNH4vLly6bFNmfOHMTGxmLs2LHo378/fv/9d3z11Vdo27YtAgIK78+8M4fdYlSu0j2u070uJy8d42RAPTfoLK963VXnNXTXr3uexR/7FkarVasWxo0bh2PHjmHy5MnYvHkz7rnnHgwfPtzUnMPxU/HIPc6YdZ9Rtbzu+1y621DVNk61vL+thfSmTXcnHtX6da+FKi5zw/46flq7di2aNm2Kbt26oUqVKvj111+xc+dODB06FOXLlzclJqMwh91i1lyfaluvu63RvQbF39o+3es6/XkNkG7+Fo/R6tati/feew/Hjx/HlClTsHPnTtSpUwePP/44UlNTzQ7PJTPb/dKlS+PIkSOmrjfSpWrVqrj77ruRkpJi1wddunQpnn76aZQqVQorVqxAdna23f7ExERfhks+EBYWhnnz5iEgIACDBw/GwYMHfR5D1apVER0djaysLOzcudPnxydzFbY21x/iPXz4MP7zn/+gQYMGGDx4sMMyJUuWxIcffoj09HQMHz7cxxEWPikpKRg/fjwaNmyIgQMHOiwTGBiIf//73z6OzH/kvu/+8Y9/uFXeqDGS7mcMC7Pi0F/mZ9O/LFu2DEFBQZgyZQqAW2s4srKyTI5KjWpblpeOddbOsO0jIiIiIiIiIiJP7N27Fw899BCaNWuGy5cvY8GCBfjjjz/wn//8x+lccmHC9YOumbUGXvW4up+T8bdnmlXvmel+Zlf3c3dFaY16UV9XGBsbi9dffx1//PEHvvvuO1y9ehUJCQlo3749du3aZUpM/H6u4pHHzPq+ZdXj+lt7prpuW/e6fN35ypv3iT88J6D7GTJvyhcm/H4uIiIiIiIiIiIiIqLiqfB+uzQRERERERERERERERERERERERERERERERERERERERERERERERERERERERERUSFw5coVDBw4EO3bt0dcXBz27NmDadOmoW7dumaHZpjcH+Revny5w/0tW7bE2bNnfRmSX1m1ahUAoFGjRnb7cretXLnSrbry/vhsQeLi4hAcHIyUlBS7fSkpKbBYLDbvwzNnzgAAKlasaFc+OjoaALBu3boCj3v16lVkZGSgTp06sFgsHtf/ww8/IDs7GwkJCQUeE1A/z56+3unTp2Pv3r2YMGGCW3Hl5+z8OHPx4kUcOnQI9evXR0REhNfxeBu/s3hUr5dR8RQGgYGB6Nq1KzZs2IDvv/8e+/btQ3x8PKZPn+7TONatW4f4+HjMmzcPH330Efbs2YMnnngCYWFhPo1DB+ahW4zMN444a7/atm2LF1980a58eno6Zs2ahSFDhthsV20vVPOZ7vPgjGr7blS+9AWV/ofq+8EZZ/lG9f2gWn9hFxMTg7fffhuHDx9G165d8dRTTyEpKQl//fWX2aEVOjNmzEDdunWxa9cuLFy4EBs3bkS3bt0QGBhodmiGYO50zawxnDNGjwlU6/e2vFHxeJpjdI2ZdPeBdI+hVemu39/UqVMHU6dOxb59+1C3bl107NgRTz75JK5cueLTOL755hvUqVMHK1euxLRp07Br1y4MGDDAkLbFXzAn3WJU7lGtR/WzbVR/3xnd83W6x6POGDWf5q9tsUqu9UXfqDhJSEjAggULsG3bNlSoUAGtW7fG0KFDce3aNbNDoyLk5MmT6NixI/r27YtWrVrh4MGDmDJlCurVq2d2aIZin8S1wtJXMSu3GjU3bFTfw0hmjD3MOj9Gzc3rnscpTCIjIzFy5EgcPnwY48ePx+zZs1GnTh0sXbrUp3GkpaWha9euePzxx9GpUydrPFWqVPFpHLowh92i+/6gUeM63etycukaJwPquUFnedXrrjqvobt+3fMs/ti30CU0NBSPP/44du/ejYkTJ+Kbb75BvXr1sGbNGp/GwfHTLcUl9zhj5n1GnWs7dK/lVI1HtY1TLe9vayE9bdPdjceonKF7LVRRnhv2h/FTRkYGnnvuObRq1Qply5bFli1b8N1336FFixY+i0E35rBbzJrrM6r/a1Rbo3sNir+1fbrXdaqW99UaIN38LR6dwsLC0L9/f+zatQuzZs3Cr7/+ijp16mD+/Plmh+YU23192rdvDwBYsmSJzfYlS5YgKSkJDzzwAC5cuIDffvvNZv+yZcusf0tFS2JiIkaPHo309HT07NkTmZmZPo9BRACgSDwfRaTbpEmTkJWVhZ49e7os16JFC1SuXBnLly/H77//7qPoCqcpU6YgJyenwHN6//33Q0QQFBTko8jMlZaWhokTJ+K+++7DnXfeibfeegu33367V3WqjknMesawMCgO/WV+Nv3L9OnT8fjjj6NRo0aoV68ezpw5g8WLF5sdVoGMaMt8vc6abR8REREREREREanIzs7G2LFj0aBBA6SlpWHZsmX49ddf0a1btyI1b1oc5sW9YdYaeNXj6n5OprA806x6z0z3M7tGfz+xu/H72zrNXMVpXWHu9yyvWbMGK1aswMWLF9GoUSO8/vrryMrK8lkc/H6u4pPHzPq+ZaOOa1Z7prpu21fr8vMzKl95er385TkB3c+QeVq+MOP3cxERERERERERERERFR8BZgdARERERERERERERERERERERERERERERERERERERERERERERERERERERERERFRUXbx4Ee3atcP333+P7777DgsWLMBdd91ldliGa9GiBaKiorB06VJ06NABv/zyC3Jycgr8u3PnzuGFF15AzZo1ERoaiipVqqBt27aYOXMmMjIyHJYLCQlB2bJl0aFDB6xevdpaZtGiRbBYLNZ/Bw4cQK9evRAZGWndlvuDxmlpaRg+fDiqV6+OkJAQVKhQAd27d8eOHTuMPzmA9UdXq1SpYrcvJiYGAHDw4EHDj1upUiVMmDABO3fuxKuvvoq0tDScP38e7733HlasWIExY8YgNjbWWr58+fIA/v4x2rzS0tIAAH/88UeBx/32228BAK+99prNdtX6t23bBgAoW7YsXnzxRVStWhUhISGoVq0ahg8fjvPnz9vUoXqePXm9J06cwIsvvojp06ejdOnSdn/nDmfnJ7/Lly9j/fr16Ny5M6KiojB79my7MqrxeBN/QfGoXi9v4ymskpKSsH37djzzzDMYOHBgge8DoyQnJ6Nt27aoU6cOdu/ejaeffhrBwcE+ObYvMA/dojvfuNt+5ZoxYwZuv/12/OMf/7DZrtpeqOYzT8/DnDlzcO+99yI8PBwRERFo0aIF5s+f79ZrBdTPj1H50tP4vX29qpy9H/IrKN+ovh9U6y8qKlWqhIkTJ2LNmjXYu3cvWrRogRMnTpgdVqHx+uuv48knn8SgQYOwY8cOdOnSxeyQDMfc6ZpZY7j8dIwJVOv3pLynOcbTNtpVjvHlmKmgeFT7QLrH0LncvV6e1l/Y3Xnnnfj666+xaNEi/Pjjj2jbti0uXLjgk2O/8847eOSRR9CrVy/s2bMHjz32GAIDA31ybF9iTrrFqNyjWo9Rn+2C+vvutjW65+t0j0fzM3o+TXdb70l5X87v6e6LFGb33nsvFi9ejLlz5+Krr75CYmIiLl68aHZYVATs378f999/P44cOYJff/0V06ZNQ7Vq1cwOSwv2SVwrLH0VX+XW/IyaG/a07+Fvc8O65tp1nx9v75XrnscpzMLCwjB8+HDs27cPCQkJ6NixIyZNmuSTY6empqJly5bYuXMnVq9ejc8//xxRUVE+ObavMIfdomtO1+hxne51OYDecbK/Meq6O5vX0F2/p+XdzW3ezmsURkFBQRg4cCB2796N+vXro127dli4cKFPjs3xU/HLPc74y31GTxl1n0t3G6raxqmW99VaSJ1tuko8RuUMo9dC5Soua30A88ZPly9fRocOHTBnzhzMnDkTS5cuRYMGDbQf19eYw24xa67PGdX+sq62xp14VNpWX7V93s7FGbWu09vy7sSjM9d6Ur64zvUFBASgT58+2L17N/r06YO+ffti7NixZoflkGq7P2HCBGtbXKVKFWzevBlt2rRB6dKlUbJkSbRu3Rrr168v8Lj52/rMzEyb+q9evYr169db9wcFBRn2mn0lMTERALB06VLrtvPnzyMlJQXNmjVzuH/Pnj0oWbIkatSoYd3mSa5LSUlBx44dERERoXRdXMl/zf744w/07t0bZcqUQWRkJDp16oQjR454HZM7fYU333zTGkdCQoJ1+5IlS6zbc3OMN7GnpKSga9euiIiIQHh4OFq0aIF169Z5egoB3Fp73a5dO+zatQvDhg1z62/cOSfuOHbsGFJTU3Hbbbehdu3aNvvcfZ9dv34dY8aMQVxcHEqWLIly5cohKSkJP/zwA7KzswF43k540k9051q6E7PqeXBE5XVfvHjR5nVYLBa8+eabAICsrCyb7T169Cjw2AVx1ua6e37cbZuN6utPnTrVabz5j+Ns7JGVlYWvv/4aDz74IKKiolCiRAnUrVsXH330kVvjGwBYs2YNACA+Pr7Asrll1q5dq9xGeXJ+jR5z/fnnn+jduzdKly6NyMhI9OvXDxcuXMAff/yBpKQklC5dGtHR0Rg4cCDS09PtXr+7n51ff/0VAFCvXr0Cz2l+/pAfjOynXLt2DfPnz0fHjh1RuXJlDBs2DBaLBZ9++ilSU1Mxd+5c5ToBz8cYhX2+Vqfi0F/25LOp+nkDzM85vmjrVOa0HDl//jySk5PRv39/AMCAAQMAANOnT3dYPm9/tWTJkmjSpIn1WY7c4z311FNKr0GFkW2ZGfMHbPuIiIiIiIiIiMhd169fR8+ePTF+/HiMHz8emzZtwoMPPmh2WFpw/aBrZs0rqh5X9zOm/v5Ms6f3zIx+ZtfdeHQ/w+2P6zSL67pCAGjTpg02bNiACRMm4IMPPkD37t1t2kld+P1crhW1PGbW9y0bdVyz27P8nK3b9sW6fEeMyleeXC9/ek5A9zNknpQvKvj9XERERERERERERERExYAQEREREREREREREREREREREREREREREREREREREREREREREREREREREREREZEWHTp0kKpVq8qBAwfMDkW7tWvXStWqVQWAAJCKFStKnz59ZP78+XL16lW78qmpqVKjRg2JioqS5ORkuXz5spw+fVrGjRsnAOTDDz+0KVepUiVJTk6WS5cuyYEDB6R79+5isVjkiy++sKm3S5cuAkBatmwpq1evlqtXr8qmTZskMDBQ0tLS5NSpU1KtWjWpVKmS/PTTT5Keni579uyRli1bSlhYmGzYsMGmvtatW0u5cuVk48aNHp+bBx98UADIpk2b7PYdOnRIAEiDBg2U642JiZHAwMACy33zzTdSpUoV67UpX768TJs2za7cJ598IgBk2LBhdvsaNmwoAKRRo0Yuj3X69GmpVKmSPPXUU17Xn3sto6KipE+fPnLkyBG5cOGCzJo1S8LDwyU2NlYuXrxoLa96nj15vYmJiTJkyBDr/+fMmSMAZNy4cS7PSy5X5yev3M8BAGnVqpXs2rXLYTnVeDyN3514VK+XN/EUFXPmzJGgoCCZNGmS1uPs27dPQkJCZNCgQZKdna31WGZiHtKXb0Tcb79y5eTkSGxsrHz22Wd2+zxpL0Tcz2eenIfmzZtLv379ZOvWrXLlyhVJSUmRfv36Oc0T+ameHyPzpSfxe/N63e1/5OXq/ZCXu/lPxP33g6f1FyWnT5+WOnXqSP369eXmzZtmh+P3pk2bJoGBgTJjxgyzQ9GOudM5s8dwIvrGBKr1q5b3NMd42kYXlGN8NWZyJx7VPpDuMbSI2vXytA9XlBw+fFiqV68ubdu21X6sWbNmSUBAgHz++efaj+UPmJOMyz2q9Rjx2S6oLVZpa3TP1+kej+alYz5Nd1vvSXlvcqeOvpG34/uiYN++fVKlShVJTEw0OxQq5C5evCgxMTGSkJAgFy5cMDscn2CfxLnC0lfxRW7Nz8i54VwqfQ9/mxv2xVy7rvPjzb1y3fM4Rc1bb70lFotFFi1apPU4WVlZ0qhRI7n77rvl1KlTWo9lNuYwPXO6OsZ1utfliPhmnJxL9b6h0eWNuO6u5jV01+9JeU9yrSfzGkVBTk6ODB06VIKDg2Xnzp1aj8XxU/HMPc74w31GT8qLGHufy1dtqGob5255X6yF1N2mezL+8CZnGL0WKldxXeuTy1fjp06dOknlypVlz549Wo/jD5jDzJvrc0S1v6yrrXE3HpW21Rdtn7dzcTrWdXpS3t14dOdaX94nLUq++OILCQwMtGvnjFa3bl157bXXlP9Otd0XEYmPj5fw8HC5//77ZcOGDXLlyhXZvHmz1KtXT0JCQuSXX36xKx8TE2NXT25bn5GRYbM9PDxcmjdv7jRm1XHJ9u3bBYB06dLF6T53/rmKKb+MjAwpUaKEREREWNclf/XVV5KUlCQit9ZYAZCmTZta/2bChAny7LPPWv+vmuvi4+MlIiJCWrduLevWrZP09HSX18WT85V7zbp06WK99suXL5cSJUpI48aN7cqrxKTaV3D2PmnYsKFERkZ6FfuhQ4ekTJkyEhMTI8uWLZP09HTZtWuXtGvXTqpXry6hoaFunUsRkc2bN0tERIT1/2lpadbP3Ny5c63bN27caBe36jlxdO1u3Lgh27dvl+bNm0tISIjMnj3b5m9U3mdPPfWUREREyLJly+TatWty+vRpGTlypACQ1atX29Sr0k542k9051q6G7Pq580ZldedmJgoAQEBcvjwYbt67r//fpk3b57NNm/bK0dtrso1ddU2G93Xdxavu2OP5ORkASBvv/22nD9/XtLS0uTjjz+WgIAAGTlypMPrlj9PRUdHCwD5v//7P4evOa++fftaj1fQ+XLWRrlzfnWNubp37y5btmyRK1euyOzZswWAdOjQQbp06SLbt2+X9PR0mTRpkgCQ559/3qYOlc+Oyjl19Pr9IT+4qr8gWVlZsmTJEunbt6+UKlVKAMidd94pb7zxhsN2QJU380NGz9empKQIANmxY4fbf6NCd/35FfX+sqefTVdxOPq8mZ1zfNXW5T2mqzznyCeffCKtW7e2/j8tLU2Cg4MlKChIzpw5Y1PWUX91z5490rZtW6lQoYJdf9Wovo6utsxX66zz0vmcuSfGjh0rsbGxPjseERERERERERG5b8CAAVK2bFlZv3692aH4BNcPOmfWGnhPj6vrGVN/fqbZ03tmRj+zqxKP7me4/XGdJtcV3rJp0yaJjIyUPn36aD0Ov5+r+OUxs75v2Yjjmt2e5edq3bYv1uXnZ2S+8uR6+dtzAiqvVzV+T8oXRfx+LiIiIiIiIiIiIiKiImkmzI6AiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKgo+umnnyQgIMDhD4YWVZmZmTJr1izp0qWLlC5d2vojo5GRkfLll1/alH388ccFgHz99dd29bRv3976Y8K55fL/fWZmplSuXFlKlCghp0+ftm7P/THhxYsXO4yxf//+AkDmzZtnsz01NVVCQ0OlYcOGNttbtmwpZcuWtfuRYRWufjz24MGDAsDuuO6IiYmRwMBAp/tzcnJk4MCBEhwcLP/973/l9OnTkpaWJpMnT5YSJUpI79695ebNm9byGRkZ0rBhQwkODpaJEyfK2bNn5c8//5ShQ4dKVFSUAJAWLVo4Pd7Zs2fl3nvvld69e0tWVpbdftX6ExMTBYDUqFHDJk4RkTfffFMAyL///W/rNtXzrBrPlClT5I477pArV65Yt6n8yG1B5ye/69evy/79++Xpp5+WwMBAGTt2rM1+1Xi8jb+geFSvl7fxFBWvvvqqlClTxuY8GK1Tp07SpEkTyc7O1nYMf1Hc85CufKPafonc6geVLl1a0tPT7faptheq+czI89CkSROndeVSPT9G50tv41ctX1D/wxFX74f8Cso3qu8H1fqLqkOHDklYWJhMnTrV7FD8WkZGhpQrV05eeukls0PxmeKeO50xawyXn9FjAtX6vS2flwP2oLAAACAASURBVDs5xpP6XeUYX4+ZCopHtQ+kewztiqPrZWT9hdmWLVskMDBQFi1apO0YmZmZUr58eXn++ee1HcMfFfecZFTuUa3HiM+2Sn8/L0dtje75Ot3j0fyMnk/T3darlvc2dxrdN1KNvyjbtGmTBAYGyvfff292KFSIvfjiixIdHS3nz583OxSfKu59EmcKS1/F17nV6Llhb/seefnL3LBK/WafH2/n5nXP4xQ1Tz75pFSpUkXr/ctZs2ZJaGio7N+/X9sx/Elxz2G65nSNHtfpXpeje5ycn2puMLq8Edfd1byG7vqNKJ/LUW4zMncWVjk5OdK8eXNp166d1uNw/FQ8c48z/nKf0ei1HbrXcqrGo9rGqZY3cy2kEW26ajze5gzda6GK61qfXLrHT4sXL5aAgABZt26dlvr9UXHPYWbN9Tmi0v/1xbpLI9egmNn2uTs3aOS6TiPiN/L8u+IP90mLmlGjRknZsmW1Pg9Rt25dee211zz6W5V2X0QkPj5eAMj27dtttu/atUsASHx8vF35mJgYu3py2/qMjAyb7eHh4dK8eXOn8aqOS7Zv3y4ApEuXLkr7ch09elQAuIzJkdw+89q1a0VEZMCAATJx4kTr/po1a0pAQICcO3dORG7ljR9//NG6XzXX5V6XjRs32mx3dl2ccXVOcq9ZcnKyzfYePXoIAElLS/M4JtW+grP3ScOGDSUyMtKr2Hv27CkAZMGCBTZlT548KaGhoRIaGmpXvzObN2+WiIgIm20bN26U4OBgCQ8Pt87Pb9y40S5u1XOSe+0c/evWrZscPnzYLj6V91mNGjWkWbNmdnXExsbK6tWrbbaptBOe9hPduZbuxqz6eXNG5XUvXbpUAMiQIUNsyq5bt05iYmLkxo0bNtu9ba8ctbkq19RV22x0X99ZvO6OPZKTk6VVq1Z2Zfr27SvBwcFy6dIlm+2O8lR0dLQAkN9++81pjHnrBSDvvPOOdZtqG+XO+dU15vrpp59syteuXVsAyJo1a2y216hRQ+666y6bbSqfHZVzmpc/5QdX9RekcuXKAkDKly8vQ4cOtcuNRvB0jGT0fG1KSooAkB07drj9Nyp01+9IUe4ve/rZdBWHo8+b2TnHV21d3mO6ynOONGjQQGbPnm2zrVu3bgJAJkyYYLPdWX/1r7/+kpIlS9r1V43q6+hoy3y9zjqXrntVnho7dqzExsb67HhEREREREREROSeTZs2icVikR9++MHsUHyquK8fdMasNfCqx9X9jKm/P9Oses9M9zO7Rj93pxq/v63T5LpCW0uWLBGLxWJdX2Y0fj9X8cxjZn3fsrfH9cf2zNW6bd3r8vMzOl+pXi9/e05A9zNkzFd/4/dzEREREREREREREREVOTMDQEREREREREREREREREREREREREREREREREREREREREREREREREREREREREREhlu5ciWaNGmCpk2bmh2Kz4SGhuKxxx7DokWLcP78eaxcuRKPPPIIzp07h759+2L79u3WsgsXLgQAdOjQwa6en3/+GSNGjLAp17FjR7tjtWnTBhkZGVi6dKldHU2aNHEY46JFixAQEIBOnTrZbI+KikLt2rWxdetWnDhxwrr9l19+wfnz53H//fe7fO179uyBxWKx+ffss88CAMqUKQMAuHr1qt3f5W7LLWOkOXPm4IsvvsDTTz+N559/HpUqVUL58uUxaNAgvPLKK/j6668xceJEa/mwsDCsXr0azz33HCZMmIDo6Gg0bdoUIoJvv/0WwK3z5MjVq1eRmJiIe+65B/PmzUNgYKBdGdX6w8PDAQBt27ZFUFCQTV1JSUkAYHPtVc+zSjzHjh3DSy+9hOnTp1vjUuHO+ckvJCQEcXFx+Pzzz9G5c2eMGTMGK1as8Cgeb+MvKB5A7XoZEU9RMWzYMFy8eBFbt27VUr+IYO3atXj66acREFD0f56gOOchQE++8aT9AoCPP/4Yjz32GEqVKmW3T7V9V81nRp6HHj16AACSk5Md7lc9PzrypTfxe1veXa7eD/kVlG9U3w+q9RdVtWrVQrt27bB69WqzQ/FrO3bswPnz5zFs2DCzQ/GZ4pw7/XEMl5+RYwLV+o0on5c7OcaT+p3lGDPGTK7iAdT7QLrH0K44ul5G1l+YNWzYEM2aNcPKlSu1HWPHjh04e/astV0tLopzTgKMyz2q9Rjx2Vbp7+flqK3RPV+nezyan5HzaZ6Ud8Xb8aIZ83u6+yJFSdOmTXH//fcXi/Eu6bNixQo88cQTKFu2rNmh+FRx7pP4Ypysu6/iy9yqY27Y275HXv4yN+xu/f5wfrydm9c9j1PUvPDCCzhx4gT279+v7RirV6/GAw88gLi4OG3H8CfFOYcB+uZ0jR7X6VyX44txsr8x4rq7mtfQXb8R5XM5ym1G5s7CymKx4JlnnsGvv/6KmzdvajsOx0/FL/cUhvuMnjDyPpfuNlS1jVMtb+ZaSG/bdE/i8SZn+GItVHFd65NL9/hpxYoVaNy4MZo3b66lfn9UnHMYYN5cnyPu9n99te7SqDUoRsTji/tiRq7rNCJ+I8+/K4XxPqm/GzFiBC5cuIBt27aZHYpDKu1+rvDwcNx777022+rWrYvKlStj586dSE1N1RavSptupsTERADAkiVLAADLli1D+/btrfvbt2+PnJwcLF++HBkZGfjtt9/QqlUr637VXAfcalvzP6Op47o0btzY5v9Vq1YFAJw6dcqurLsxedpX0BF77jXLvYa5KleujNjYWK9juO+++zBhwgRcvXoVPXv2REZGhsNynp6TLl26QEQgIjhx4gR69+6NhQsXYsqUKXZlVd5n7du3x4YNGzBo0CBs2rQJ2dnZAIADBw7YvHdzudtOePo63bmW7sbsyefNGXdfd7t27VC3bl3MnDkT586ds5Z9//33MWzYMAQHB7t1PG+oXlNnjO7rF3ScgsYenTp1cvhMSHx8PG7evIm9e/cWeKzKlSsDgM21cSa3TO7fGE33mKtRo0Y2/899Hfm3x8TE2LXzKp+d3HrPnj3r4tXa86f8YISQkBCEhYUhLCzM0Hpz6/ZkjFSY52t9pSj3lz39bKoyO+f4qq3LSyXP7dq1C4cOHcLDDz9ss33AgAEAgBkzZthsd9ZfrVChgsP1H0b2dQDj2jIz5w/Y9hERERERERERkTtWrFiBWrVqWdfFFhfFef2gP66BVz2u7mdM/fmZZkDtnpkvntk1+rk71fj9aZ0m1xXay71+utb98/u5il8eA8z7vmVvjuuv7ZmrdeQ61+XnpyNfqVwvf3xOQOczZMxXtvj9XERERERERERERERERU/R/5VeIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiITWCwW5OTkmB2GaYKCgvDAAw/gyy+/xKhRo5CdnY0FCxYAAK5fv45Lly4hLCwMpUuXdlpHQeUqVaoEADh9+rTdPkc/RJpbX05ODiIiIux+xH7btm0AgEOHDim/3jp16kBEbP7l/qBqXFwcANj8SHGukydPAgBiY2OVj1mQJUuWALj1o+n5tWnTBsCtH27Oq3Tp0nj//fdx9OhR3LhxA6mpqfj000+tP3LboEEDu7qysrLQs2dPxMTEYNasWQ5/cNeT+qtXrw4AiIyMtKunYsWKAIC0tDTrNk/Os7vxJCcn49KlS2jVqpXNe6Zfv34AgH//+9/WbYcPH/b4/DiT+yP3P/74o0fxeBO/O/EAatfL6HiKAovForV+EdFavz8qbnkIMD7feNp+HTx4EMuWLcOQIUMc7ldt31XzmZHnITo6GgDw119/2e1TPT+68qWn8RtR3h0FvR9ccZRvPOnfqNRflImI9nxT2OWen+KYN4Hilzv9cQznirdjAtX6jS6vmmPcqd9VjjFjzGR0HwjQO4Z2xdH1MrL+wk53Ti3u+QgofjkJMC73qNbj7Wfbm/6+s9ygc75O93jUFW/n0zwp74q340Wz5/d090WKAo4ByVvF/T4nUPz6JL4YJ+vuq/gqt+qaGzay7+EPc8Pu1u9P58eouXnd8zhFgS/GvBaLpdiOrYtbDgN8M6drxLgO0LcuxxfjZH/j7XUvaF5Dd/3els/LUW4zMncWZiKCgIAA7XO6HD8Vr9xT2O4zusPo+1y621DVNk61vJlrIb1t0z2Jx9OcYcZaqOK21gfQP34KCAgotmMnoPjlMMC8ub783O3/+qqtMXINihHx5KXjvpjR6zq9La/j/DtT2O+T+qPcPFIY7k26avfzKlOmjMO/z+0LFqf7zs60b98eALB06VLs3r0bYWFhqFmzpnV/YmKidf+aNWvQqFEja97zNNdFRkY6fJ8ZfV0iIiJs/h8SEgIADsf+7sTkTV/B6NivX7+O9PR0hIWFoVSpUk7j9tbw4cPRu3dv7NmzB88++6zdfqPOSUxMDGbOnImaNWvi/fffx5YtW+yO4e777NNPP8Xs2bPx+++/o02bNrjtttvQvn17LFy40OGx3WknvHmd7rwP3YnZ6L6lSvs4YsQIXLt2DZ999hmAW/l+1apVGDRokFvH8pbqNXXE6L6+p8fJ69KlSxgzZgzq1q2LsmXLWq/lSy+9BAC4du1agcdr2bIlAGDHjh0Flt25cycAoFWrVgWWVeWLMddtt91m8/+AgAAEBgaiZMmSNtsDAwNtPl+qn53cc7pr1y53XrrXr0uVSm7zxLFjx/Dzzz+jdevWmDRpEurXr4/atWvjnXfewZ9//mnIMfJSGZMU1vlasxS1/rInn01PmJ1zfNXW5aWS56ZPn4709HSEh4fb1N+5c2cAwN69e/Hbb79ZY3LVXy1btqzN/43s6xjdlpk5f8C2j4iIiIiIiIiI3FHc1w4CxW/9oD+ugVc9ru5nTP31mWZHXN0zM+OZXaOeu1OJH/CfdZpcV+hYblujA7+fq/jlMcC871v29Lj+2p65s45c17r8vHTlK5Xr5Y/PCeh8hoz5yh6/n4uIiIiIiIiIiIiIqGgJMDsAIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIqKi6IEHHsCWLVuwceNGs0PRbv369dYf9nWkdevWAIALFy4AAEJDQxEREYHMzEykp6c7/buCyp05cwYAEBUV5VacoaGhKFOmDIKCgnDz5k27H7HP/Zcbr1Fy69u6davdvtxtuT/CaqTcH4515cqVK27VtW7dOgBA9+7d7fYNHjwY169fxzfffIOgoCDr9lq1amHTpk0e15+QkAAASE1NtSuf+2Pped93Rp7n/PEMHTrU4Xtlzpw5AIBx48ZZt9WqVcumLiPOT2hoKADg/PnzHsXjTfzuxAOoXS+j4ynMPvroI5QtW9btH3hWZbFY0LJlS0yaNAnZ2dlajuEPmIduMTrfeNp+ffzxx/jHP/6Be+65x+F+1fZdNZ8ZeR5OnToFAKhYsaLdPtXzoytfehq/EeXdUdD7wRVH+cbI/o2j+ouqQ4cOYfny5Yb3c4ua+Ph4REZG4uOPPzY7FO2YO10zawznirdjAtX6jS6vmmPcqd9VjjFjzGR0H8gVI8bQrji6XkbWX5ht3rwZGzdu1NoGxMfHo0KFCvjwww+1HcOfMCfdYlTuUa3H28+2N/191dxgxHyd7vGoK97Op3lS3hVvx4tmz+/p7osUdps2bcLGjRvRtm1bs0OhQqxt27aYMWNGsZg3AdgnKUhh6av4Krfqmhs2su/hD3PD7tZfGM6P6ty87nmcomDChAmoWrUq7r77bm3HaN26NVatWoX9+/drO4Y/YA67xRdzukaM61zxdl2OL8bJ/sbb617QvIbu+r0tn5ej3GZk7iyscnJy8Nlnn6FFixY2nx2jcfxkq7jkHmf88T6jO4y+z6W7DVVt41TLm7kW0ts23ZN4PM0ZZqyFKk5rfXLpHj+1adMGW7ZssV6Loow57Baz5vryc7f/66u2xsg1KEbEk5eO+2JGr+v0trwvz39hv0/qjz788EOUK1dO2/MQnlJt9/M6d+4cRMRue25f0Ju5dIvF4vHf+pO7774bVatWxdatWzF37lwkJiba7G/dujVCQkKwdOlSLFmyxGa/p7nu0qVLDmMx4rp4yp2YPOkrBAQE4MaNG3ZlL1686FW8oaGhKF26NDIzMx328Y3sZ0+dOhV33XUXpk+fbm0j88ZhVP8pLCwMb7/9NkQEr7zyis0xVN5nFosF/fr1w4oVK3Dx4kUsWrQIIoLu3bvjv//9r91x3WknjO4n5udOzEb3LVXaxz59+qBSpUqYOHEirl+/jg8++AD9+/dH2bJlPXq9qlSuqbO2Wfc1dPc4eSUlJWHcuHEYOHAgDh48iJycHIiIdb2po+uT3+DBgxEUFIRvv/3WZbl169bh1KlTSEpKwu23327drtpGeXp+3S1n1HXIf0yVz07uOV2wYIHLel9++WUEBAQgJSXFr/JDLk/7KYGBgWjfvj3mzp2Lv/76C3PnzkW1atUwZswY1KhRAy1atMDkyZMNyzMqY5LCOl/rC8Whv+zJZzOXyuetMOWc/MfUfR/l5s2bmDdvHtavX++w7hEjRgAAZsyYYY3JVX819z2m4zUY3ZaZOX/Ato+IiIiIiIiIiNzRpk0bHDlyBN9//73ZofgE1w+6Zta8oupxdT9j6q/PNDvi6p6ZGc/sGvncXWFYo851hQX7+eefsX//fjz44INa6uf3c9kqLnnMrO9b9vS4/tqeebOO3Nt1+Xnpylcq18sfnxPQ+QwZ85Utfj8XEREREREREREREVHRE2B2AERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERdFDDz2EDh06oFevXjhw4IDZ4WglIvjrr7+c/gDtli1bAAD169e3buvWrRsAYPHixXbl69evj+eff96m3E8//WRT5vr161i5ciVKlCiBxMREt2Pt3r07srKysH79ert97777Lm6//XZkZWW5XZ87WrZsiXvuuQcLFixAZmamdXt2dja++uorVK1aFR07djT0mADQtGlTAMDKlSvt9q1atQoAcN9991m3nT17FgEBAdYfJM91+fJlTJ06FY888ghiY2Nt9r3xxhvYu3cvvv/+e+sPzzqjWv9DDz2EmJgYLFmyxOa8AUBycjIAoGvXrtZtqufZk9erSuX8jBw5En379nW47+effwYANG7c2Kt4VKjGo3q9CJg1axbef/99vPvuuwgPD9d2nHfffRe7du3C4MGDkZOTo+04ZmIeusXIfKPSfuV1+fJlzJ49G0OHDnVaRrW9UM1nqudh6tSpaNiwoV3dIoJvvvkGAJCUlGSzT/X86MyXqvF78no95c77QTXfqL4f/C2/miE1NRVdu3bFPffcg/79+5sdjl8LCwvDhAkT8L///Q/Tpk0zOxytmDtdM2sMp7vNUq1ftbxqjvHm9bqTY3xJRx9I9xha9XpxzAccOnQIPXr0QJs2bdC5c2dtxwkNDcWHH36ITz75BBMnTtR2HH/BnHSLUblHtR5vPtvutH2qbY3u+Trd41Hd82m623pfjhfdobsvUlTt3bsXDz/8MBITE4vF6yV9Ro8ejaCgICQlJeHChQtmh6Md+ySuFZa+iu7ygN65YdW+h7/let1z7brPj2pflHPPnhs3bhxmzZqFTz/9FAEB+n5evU+fPrj33nvRrVs3nDx5UttxzMYcdotRuUr3uE73uhxVhX3Oz5vr7s68hu76Vcur5jbV3FnU5OTkYOjQodi6dSsmTJig9VgcP9kqLrnHGbPuM3pDx30u3W2oahunWl53jvS3Nt2T+nWuheJ462++GD+1b98enTp1Qq9evbB7924tx/AXzGG3mDXXl5e7/WWdbY0n8ajQ3fZ5M9enY12nv60B8re506Js8uTJ+OCDDzBhwgSULFnS7HBseNLu58rMzMTmzZtttu3evRunTp1CfHw8oqOjPY6rZMmSuHHjhvX/d911F6ZMmeJxfWZKTEyEiODjjz9G+/btbfaVKlUKzZs3x6lTpzBz5ky7POhJrrty5Qp27txps82o6+Ipd2NS7StER0fbzWufPn0ax44d8zrmDh06AACWLFlis/3s2bOGPv9aqlQpfPfddwgPD8dnn31mt9/I/lPPnj1Rv359rFy5EsuXL7duV3mflSlTBikpKQCA4OBgPPjgg1i0aBEsFotdjID77YTR/cS83I3ZyL6lSvsYGhqKIUOG4K+//sIHH3yAefPm4bnnnvPkpXpE5Zq6apt1XsO83Bl7ZGdnY/369YiKisLw4cNRoUIFWCwWAEBGRobbx4qNjcXrr7+Obdu2YfLkyQ7LXLt2DSNGjEBkZCT+97//2exTbaPcOb++HnO5Q+Wzk3tOt2zZgunTpzus78CBA5g8eTJ69eqFuLg4AP6VHwBj+iklS5ZEnz59sHjxYpw8eRIfffQRbty4gaeffhrR0dFOxw35GTU/VBjna32lOPSXPf1sAmqft8KWc/LSfR8lOTkZ5cuXR7NmzRzuf/LJJwEAX375pTWXOeuvnj59GgcPHvTJazCqLTML2z4iIiIiIiIiInJHkyZN8MQTT+Dxxx/H2rVrzQ5HO64fdM2seUXV4+p+xtTfnmn25J6Zzmd2ffH9xP60Rp3rCj23ceNG9O3bF3379kVCQoKWY/D7uWwVlzxm1vcte3Jcf2rP8tdZ0LptX6zL15mvdPdrdF+v4v5csK/w+7mIiIiIiIiIiIiIiIooISIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLS5evCgJCQlSvnx5+eabb8wOR5u1a9cKAKlatarMmzdPTp48KZmZmXL06FF5//33JSQkRBo2bCiZmZnWv0lNTZUaNWpIdHS0/Pjjj3L58mU5fvy4PPPMM1KpUiX5888/bcpVqlRJkpOT5fLly3LgwAHp3r27WCwWmTJlik0sXbp0EQCSkZHhMNYzZ85IzZo15Y477pDFixfLxYsX5dy5czJp0iQpWbKkfP311zblW7duLeXKlZONGzd6dY42btwoYWFh8sgjj0hqaqqcPXtWBg8eLEFBQbJkyRKbskePHpWAgAABIFu3bnVaZ0xMjAQGBjrdf+HCBbnzzjslODhYPvroIzlz5oycPXtWpk6dKiVLlpSYmBg5deqUtXxaWpoAkHbt2smhQ4ckMzNT/u///k/uv/9+iY+Pl3PnztnUP2PGDAHg8l/e86Zav4jIzz//LEFBQdKlSxc5ePCgXLhwQWbPni3h4eHStGlTuXbtmsfn2ZN48pszZ44AkHHjxtntUz0/L774olgsFvnPf/4jR48etX6GXn75ZQEgDRs2tHu9KvGolvckHtXr5W38hVVGRoaMHDlSLBaLjBkzxifH/OmnnyQsLEweeughSU1N9ckxfYl56G9G5BvV9iuvDz/8UKKjo+XmzZsu41RpL1Tzmep5+OKLLwSADBkyRA4dOiQZGRmSkpIiffr0EQAybNgwm/Kq50d3vlSNX7V8fgX1P/Jy5/2gmm9U3w9G5NfCbM2aNVKtWjWJi4uTkydPmh1OoTF27FixWCzy/PPPF9n3B3NnwcwYw+keE6jWr1peNcd483rd7XOonB9vyuvoA+keQ3vSJ/B2zFeYfffdd1KhQgW5//775cKFCz455rvvvisWi0UGDRokly5d8skxzcCc9Dejco9KPSKef7bdaftU2xrd83Wqr9cX4w/V86+zrfd2vOjO+c/L6L6REfEXdrNnz5YyZcrIP/7xjyKdO8h3UlJSpFq1alKrVi1Zs2aN2eFoxT5JwQpLX0Vned1zw6p9D3+bG9Y91677/Kj2Rc24t1vYnTlzRnr16iWBgYHyxRdf+OSYqampUrt2bbn99ttl1apVPjmmrzGH/c2IXKV7XKd7XY4jRo6T81O5b6irvGrfIpe7c7q661cpr5rbPLnPXlScOXNGkpKSJDQ0VL7//nufHJPjp+KZe5wx4z6jN+V13OcS0duGqrZxnrSJOtdCmtGmu4pHtX7da6GK+1ofEd+Pny5fviytW7eWiIgImTFjhvbjmYU57G9mzfXlcqet98VzCirxOOKqbdXd9nkz16djXae/rQHy9/ukRUF6ero888wzYrFY5M0339R+vLp168prr72m9DeetPsiIvHx8RIRESFt2rSRDRs2yJUrV2Tz5s1Sr149CQkJkV9++cWufExMjN3xnbX17du3l4iICDl27Jhs2LBBgoKCZN++fdb9qm369u3bBYB06dJFaV+uo0ePCgBp3ry53faCxioLFiwQABIcHCzp6el2+8ePHy8AJDo62m6faq6Lj4+X8PBwSUhIkE2bNrm8Lq5id3VOnF2zUaNGCQDZvn27xzGp9hWeffZZASCffPKJpKeny+HDh6VXr14SExMjkZGRXsV++PBhKVeunMTExMiyZcskPT1d9u7dK4mJiVKxYkUJDQ21qcPV+dy8ebNERETYxZPX3LlzBYBd3KrnpKD3808//SQApEGDBpKTkyMiau+ziIgIadmypezcuVMyMzPlzJkz8sYbbwgAu3ZOpZ0wqp/o6Fq6G7PKeXB1vVXbR5FbfaISJUqIxWJx2RZ50145O28q19RV22x0X99ZGXfHHg888IAAkPfee0/S0tLk2rVrsmrVKrn99tsFgCxfvtzmWM7ylIjIv/71LwkMDJTnn39e9uzZI5mZmXLhwgVJTk6W+vXrS0xMjGzZssXu71TbKHfOr6/GXImJiQ7nzFq2bCnh4eE221RzlYjIK6+8IsHBwTJq1Cg5cOCAXL9+XU6cOCFTp06V6OhoSUhIkCtXrljL+1N+KOhaeevQoUPy+uuvS4sWLdwqrzrG0DF2dSQlJUUAyI4dO5T+zl/qz6u49JdF1D+bImqfN3/LOTrbOmfHdPYZ7NSpk7z33nt2seTVpEkTASBz584VEcf91d27d0v79u2lEyNGewAAIABJREFUWrVqdv1VT9prT6m2ZY4Yuc7aV22ft8aOHSuxsbE+PSYREREREREREbknMzNTevToIWFhYfL+++9LVlaW2SFpw/WDBTNrDbzKcXU/YyriX880q94z0/3Mru7n7vxtjTrXFarLzs6WDz/8UEqUKCFdu3Z1uXbCKPx+ruKXx8z6vmWV4/pbe5aXO+u2da/L152vRLy/X2rmcwK6nyEzonxhx+/nIiIiIiIiIiIiIiIqsmbC7AiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIqyq1evytNPPy0Wi0W6du0q+/btMzskw2VnZ8u6detk5MiR0rRpU6lcubIEBQVJ6dKlpVGjRvL222/L1atX7f7u7NmzMmLECKlRo4YEBwdLdHS0PPLII3Lw4EGX5SIiIiQxMVFWrlxpLbNx40aHP/rqyLlz5+SFF16QO+64Q4KDg6VChQrSrl07Wb58uV3ZFi1aSNmyZWXDhg1eniWRbdu2SYcOHeS2226TUqVKyQMPPCDr1q2zK5f7I8MWi0V27dplsy85OdnpD9x+8cUXdnWdP39eXnrpJYmLi5PQ0FAJCQmRmjVryrPPPiunT5+2K798+XLp3LmzREVFSYkSJaROnToybtw4hz/+27FjR+UfPVapP9eGDRskMTFRIiIiJCQkROLi4uSNN95w+jfunmdP4xERGTx4sMPXm5iY6PH5uXTpkkydOlUSExOlevXqEhISIqVKlZKGDRvKO++84zImd+JRLe9pPKrXy5P4C6ucnBxZuHChxMXFSUREhMycOdOnx9+wYYPUrFlTypcvLxMnTpQbN2749Pg6MQ/Z8jbfeNK+i9x6j9eqVUvGjBnjVpwq7YVqPlM5D5mZmfLtt99Kt27dpGbNmhIaGioRERHSqlUrmT9/vl151fOjO1+qxq9aXkS9/yHi/vvBk3yj8n7wJr8WZqmpqfLMM89IQECAdOnSRdLS0swOqdCZM2eOlClTRmJjY+W7776TnJyc/8/efcdFdeX9A//AAErT0JkZ6iDSlG4HAi5RkyghxdjiJrurpmzMarLZxJj8UnB3n2zMPnETsyZxE02IGk2iRlI0xgoaC4qFMipDn6GInSIM3N8fPnOXEVAGhEv5vF+veTHce+be77kD93vOPWfuSB3SHcXc2TE93Yfr7j6Bqds3tbypOaaz9TW1zdHR49PZ8t3ZBurOPnRn2gSmxt8f5OXlCQ8//LBgZmYmzJ8/X7h27VqP7n/Tpk2Ci4uL4OnpKXz22WdCY2Njj+6/JzAnGbsTuceU7RiY+r/d0XNfZ8413Xm9rjP17Yn+h6nHv7vO9Z3NDYLQ8ePfnW2jrsTf1x09elSYPHmyYG5uLjz33HP9Ni+TNLRarTBt2jTBzMxMeOKJJwSNRiN1SN2CbZKO6Sttle4q3xPXhk1pe/S2a8Pdfa29u4+PIJjWFu3Jsd2+rra2Vvjf//1fwdnZWfD29m7zXNWdzp8/Lzz00EOCubm5sGDBAqGsrKxH99/dmMOMdTVX9US/rrvn5Rh0Vz/Z1HHD7i4vCKa3LUy9ptvd2+/O6yydGWfvyxoaGoR///vfgqurq+Dr6yvs37+/R/fP/tPAzD3tkWKuaHfO7TDozrmcpsZj6jmuM+fE7poL2ZPn9I62CUzZfnfPhRqoc30EQdr+U11dnbB48WLB3NxcSExMFA4fPtxj++4pzGHGpLrW19FzfU99TqE756B057mvs+Ni3TWvs7fNAeqN46T9hV6vFz7//HPB29tbcHJyEjZs2NAj+x05cqSwdOlSk17T2fN+WFiYoFQqhZycHGHy5MmCvb29YG1tLdx9991G57d33nmn1fu+dOlSYfPmza2Wz5kzR3xdXl6eEBsbK9ja2gqenp7CypUrjfZvyjnd1ta21b7eeeed264zUCqVbdZBEG5//hcEQbh06ZJgYWEhxMfHt7n++PHjAgDhiSeeaHN9R3Jdy+OsVCqFw4cPCwkJCYKdnV2b78utYm/vmLSVnw3H4ebl999/f6diEoSOtRVaHtt58+YJcrlcsLa2FmJiYoQjR44IUVFR4r5feuklk2M3UKvVQnJysjBkyBDB2tpaGDVqlJCWlib85je/Ecv/4Q9/aPd4VlVVtdp+VFRUm++zIAjC008/LTg5OXX6mLT13s2YMaPV9mJiYsT1EyZMEASh422qrKws4cknnxSCgoIEGxsbwdHRURg7dqzwySeftJpD3tHzhCn17Mx7aUrMHT0Ot/rfN7XeBvPnzxcACHv37m1zfVfOV7c655pyfG53br5Tbf3b5YiO9D2qqqqEJ598UvD09BQsLS0FNzc34YknnhBefvllo//H9vLUzY4cOSI8/vjjgre3t2BlZSXmyWXLlgmXLl1q8z3r6Dmqs8f3Tve5li5dKhw5cqTV8r///e/C/v37Wy1//fXXxe2Z0i8zOHz4sDB37lzxPbK3txfGjh0rrFixQrh+/Xqr8r0pP9zuvepJpvYx7nTftT15eXkCACErK6vTdZNy+y0NhPZyS6b+b5pyrusNOae7z3W3y3M3/w+WlJQYlRszZkyrY1xQUNBqe25uboIgGLdXbWxshPHjxwt79+4V4uPjBRsbm1bb6sz5uqd1xzzrnjr3ddVbb70lDB8+vMf3S0REREREREREHaPX64W//vWvwqBBg4TIyEjhxx9/lDqkbsH5gx0jxRx4U/YrCN3/GVNB6D2faTZ1zKy7P7Pb3Z+7621z1Dmv0DTbt28XRo8eLVhZWQlvvfWWoNfre2zfvD/XwMtjUt1vuaP77W3nMwNT5m1357z87s5XBp0ZL+0NnxPobH1Nib+z5fs63p+LiIiIiIiIiIiIiKjfW2MmCIIAIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLqVj///DNeeOEF5OTkYM6cOVi0aBEiIiKkDouIiLqZXq/Hli1b8M477+DIkSN45JFHsHz5cnh5efV4LNeuXcPrr7+OlStXwsPDAy+99BLmzJkDGxubHo+FiIjuvOLiYnzwwQf48MMP4eDggL/97W+YO3eu1GH1WaWlpXjxxRexceNGREVF4cUXX8SDDz4ICwsLqUMjIqJulpWVhffeew9ffvklAgICsHz5ckyZMkWSWKqqqvDKK69gzZo18PPzwwsvvMB+HBERAQD27NmDFStWYOvWrRg1ahSWL1+O2NhYqcOifuqrr77Cq6++iqKiIjz22GN49tlnERkZKXVYRETUi1VVVeHTTz/FihUrcOXKFfzxj3/Ea6+9Bjs7O0niWbduHZYsWYLz58/j6aefxsKFC+Ht7S1JLEREdGfV1dXhyy+/xD/+8Q8UFRXh6aefRkpKCuzt7SWJh/0nIiIyVW/qP2VkZODPf/4zDh06hGnTpmHRokVISEjo8TiIiKh3qa2txfr16/Huu+/i7NmzmDt3Lv7+97/Dzc2tR/YfGhqKpKQkLFu2rNv3FR4ejvPnz6O0tLTb90VEfdNAPU90tt6fffYZVq5ciaNHj3ZTZEREPUetViMwMBBZWVkICwvrc9u/EwZqHqTeITAwEHV1dSgqKpI6FDJBSkoKUlNToVarpQ6FiIiIiIiIiIhuITc3F3/5y1+QlpaGcePGYfHixUhOToalpaXUoRERUS/V2NiIrVu34r333kNGRgbuu+8+/OMf/0BISEiPx8L7cxERUXt4fy4iIiIiIiIiIiIiogFjrbnUERARERERERERERERERERERERERERERERERERERERERERERERERERERERERERDQT33HMPsrKysHbtWhw7dgyRkZGIiYnBF198gWvXrkkdHhER3WGFhYVYtmwZVCoVZs6cCYVCgczMTGzcuBFeXl6SxGRnZ4d3330XarUa8fHxWLhwIby8vPDSSy8hNzdXkpiIiKhrmpqasGPHDjz66KPw8/PDl19+iTfeeANnzpzB3LlzpQ6vT/Pw8MD69euRmZkJDw8PzJo1C76+vkhJSUFBQYHU4RER0R127do1pKamIi4uDhERETh69Cg+/fRTnDx5ElOmTJEsLhcXF3zyySfIzc3FuHHj8Nxzz0GpVGLx4sU4duyYZHEREZE0Kioq8P7772PEiBFISEhARUUFtmzZgkOHDiE2Nlbq8KgfmzFjBnJzc/HRRx/h8OHDiIqKwpgxY/Dxxx/jwoULUodHRES9hF6vx/bt2zFnzhx4enri7bffxpw5c6DRaPD222/Dzs5Osthmz54NtVqNlJQUbNiwAX5+fnjkkUfw008/oampSbK4iIio89RqNZYsWQIvLy88++yziImJgVqtxnvvvQd7e3vJ4mL/iYiIOqK39p8mTJiAgwcP4rvvvsP58+cxceJEhISE4F//+hcqKiokiYmIiKSTlZWF559/Hh4eHvjjH/+IUaNGITs7G59++inc3NykDo+IiPqAVatW4fnnn5c6DCIiIuojysvL4ejoiMbGRqPlhYWFyM/Px8SJEyWKjIiIiIiIiIiIqH8LCgrCtm3bcOjQIbi6umLWrFnw8vLC0qVLkZ2dLXV4RETUi+Tm5uK1116Dt7c3Zs6cCScnJxw8eBDff/89QkJCJImJ9+ciIqKWeH8uIiIiIiIiIiIiIqKByUwQBEHqIIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIBprdu3fjgw8+QFpaGiwsLJCcnIwZM2bgnnvugbW1tdThERFRJ2i1WmzduhXr1q1DRkYGnJ2d8fjjj+OZZ56Br6+v1OG1UllZiY8++ggff/wxSktLER0djcceewwPPfQQPD09pQ6PiIjaIQgCjh49io0bN2LdunXQarUYN24cnn32WUyfPh2WlpZSh9gvFRYW4sMPP8TatWtRVVWF8ePHY/bs2UhOToZCoZA6PCIi6oS6ujrs3LkTGzZswNatW9HQ0ICpU6fi2WefRUJCAszMzKQOsZXz58/jP//5D1avXo1z584hMDAQs2bNwoMPPoiRI0dKHR4REXWDqqoqpKWl4auvvsLOnTthY2ODRx99FM888wwiIyOlDo8GqH379mHVqlXYsmUL9Ho9Jk2ahOnTp+P++++Hs7Oz1OEREVEPamxsxL59+/Dtt99i06ZNqKqqwpgxY7BgwQLMnDkTNjY2UofYSmNjI7755ht88MEHyMjIgFwux6xZszBjxgyMGjWqV14PICKiG8rKyrB582akpqbi0KFDUCqVmD9/Pp566im4ublJHV6b2H8iIiKDvth/ysrKwocffoivvvoKNTU1+M1vfoMZM2Zg2rRpcHFxkTo8IiLqBqdPn8bmzZuxfv165ObmQqVSYf78+fjDH/4g2bk/NDQUSUlJWLZsWbfvKzw8HOfPn0dpaWm374uI+qaBep7oaL1Xr16N77//Hl988QVSU1Pxz3/+Ezk5ObCwsOihSImIuo9arUZgYCCysrIQFhbW57Z/JwzUPEg9p7y8HHK5HL/73e/w5ptvwsnJCadPn8bChQtx7tw5HDlyBCqVSuowyQQpKSlITU2FWq2WOhQiIiIiIiIiIjJBcXExPv74Y6xZswZlZWUYOXIkZsyYgeTkZISEhEgdHhER9bDc3Fxs3boVGzZswIkTJ6BUKvH4449jwYIF8Pb2ljq8Vnh/LiKigYf35yIiIiIiIiIiIiIiGvDWmgmCIEgdBRERERERERERERERERERERERERERERERERERERERERERERERERERERERERHRQFVdXY2NGzdi3bp1OHDgAAYPHoxJkyYhKSkJ99xzDzw8PKQOkYiI2tHc3IyTJ0/ip59+wpYtW3DkyBFYW1sjKSkJc+bMwaRJk2BpaSl1mLfV3NyMPXv24PPPP8fmzZtx9epVREVF4cEHH8R9992H0NBQmJubSx0mEdGAVltbi4yMDHz33XfYunUrSkpKoFKp8Nhjj+Gxxx6Dv7+/1CEOGHq9Hjt27MCXX36JrVu3ora2FqNGjcIDDzyAe++9F2FhYcybRES9WFlZGXbu3InvvvsO27dvR11dHcaNG4fZs2djxowZcHJykjrEDjt06BDWr1+PjRs3QqfTwcfHB0lJSbjvvvsQExMDW1tbqUMkIqJOaG5uxqlTp7B9+3Z89913+PXXX2FpaYkpU6Zg1qxZmDZtGqytraUOkwgAcPXqVWzduhUbNmzAzz//jKamJowZMwZTp07FpEmTEB4eDplMJnWYRER0h5WUlGDXrl34/vvvsWPHDly+fBlhYWF49NFHMXPmTKhUKqlD7LBz584hNTUVqampyM/Ph1KpxAMPPICkpCT2rYmIegFBEHDq1Cn88MMP2Lx5M44cOQI7Ozs8+OCDmDt3LiZOnNhnxuXYfyIiGpj6S/+prq4OaWlpWL9+PX788Uc0NDRg7NixSEpKwuTJkznHlIioD6upqcGBAwfw/fffY9u2bdBoNJDL5Zg+fTpmzZqFsWPHSh0iQkNDkZSUhGXLlnXbPpYvX44XX3zRaNnSpUu7dZ9E1LcM1POEqfVevXo15s+fDwsLCwQHB+Ozzz5DZGRkT4RKRNTt1Go1AgMDkZWVhbCwsD63/a4YqHmQpPHLL79g5cqVOH78OLRaLRwcHJCYmIg333wTfn5+UodHJkpJSUFqairUarXUoRARERERERERUSc0NzcjPT0dGzZswDfffIPKykr4+vpi6tSpuPfeexETEwN7e3upwyQiojvs2rVryMjIwI8//oi0tDTk5+fDxcUFDz/8MGbOnInY2Ng+M3ec9+ciIuqfeH8uIiIiIiIiIiIiIiK6yVozQRAEqaMgIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLg/Pnz+OGHH5CWloYffvgBNTU1kMvliImJQWJiIiZPngxvb2+pwyQiGtA0Gg127tyJnTt3YteuXaiuroaTkxPuu+8+TJs2Dffeey/s7OykDrPT9Ho9fv31V2zatAmbNm2CTqeDvb09xowZg8TERCQmJiIyMhJmZmZSh0pE1K/p9XqcOHFCzDnp6emor69HcHAwpk2bhqlTp2LChAk8H0usvr4e6enp2LZtG77++mtotVrY2dlh7NixYt6MiIiAubm51KESEQ1YFRUV2LdvH9LT05GRkYFjx45BJpNhzJgxmD59Oh555BEolUqpw+yy7OxsbNq0CWlpaTh27BjMzc0RHh6OCRMmiNcWHRwcpA6TiIja0NTUhLy8PGRkZLS65jhx4kRMnToVycnJGDJkiNShEt1SbW0tfvnlF6SlpSEtLc2oj2xok8TExGDw4MFSh0pERCbSaDRivzo9PR05OTmwsLDAmDFjMG3aNCQnJyMgIEDqMLssOzsbaWlp2LZtGw4cOACZTIawsDDxWi/zGBFRzzDMy0lPT8euXbtQVlZmNC/nvvvug62trdRhdgn7T0RE/ddA6D/V1dVh586dbeaxxMRETJgwAaNHj4aVlZXUoRIRURtqampw8OBBMV/t378f169fh0qlwtSpUzF9+nSMHz++V815DA0NRVJSEpYtWyZ1KEREREQ0gKnVagQGBiIrKwthYWF9bvtERFJISUlBamoq1Gq11KEQEREREREREVEXNTc34/jx49i2bZvRfU0CAgLEe5okJCTA2dlZ6lCJiMhEV69exaFDh8TPcx05cgQNDQ3ivMJp06YhPj4eFhYWUofaJbw/FxFR38X7cxERERERERERERER0W2sNRMEQZA6CiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiMlZbW4uDBw9iz5492L17Nw4fPozGxkb4+/sjPj4e8fHxGD9+PHx8fKQOlYio32psbMTp06exf/9+7N69G/v370d1dTUcHR0RGxuLhIQExMfHY+TIkTA3N5c63DuuubkZJ0+exO7du7F7927s27cPly9fhru7OxISEpCQkIDY2FgMHz68X9afiKgn1dfX49ixY9i7dy927dqFAwcOoLa2Fj4+PuI5d+LEiVAqlVKHSu0QBAGnTp0S+3D79u3DhQsX4OjoiLi4OMTHxyM2NhYjR46EpaWl1OESEfVbRUVFOHDgAPbs2YM9e/bgzJkzsLS0xOjRo42uqdnY2EgdarfR6XTYu3cv9u3bh7179yInJwcymQwRERFiTho9ejTc3NykDpWIaECqq6tDVlYW9u/fj3379mH//v24cuUKXFxcEBsbi7vvvht33313v73mSAODIAjIzs7G3r17xb91nU4Ha2trjBkzBnfffTdiY2MRFRWFu+66S+pwiYiohcbGRmRnZyM9PV08h5eXl8PGxgZjxoxBXFwc4uLiMHbs2H7dt9Zqtdi1a5c4TlpQUABra2uMHz9eHCOOjIyEtbW11KESEfVpgiDgzJkzSE9Px+7du7Fr1y7odDrY29sjLi4OEydOREJCAsLCwvptH5n9JyKivov9pxtzTLOzs7Fnzx5xfLKqqgr29vaIjY1FXFwcYmNjER4e3m+PARFRb1dZWYnDhw9j79692Lt3L44fPw69Xo+goCDExcWJY5MKhULqUNsVGhqKpKQkLFu2TOpQiIiIiGgAU6vVCAwMRFZWFsLCwvrc9omIpJCSkoLU1FSo1WqpQyEiIiIiIiIiojusvLxc/Jz8vn37cPr0aQiCgJCQEHHu4NixY3m/ZCKiXqioqAi//vqreA7Pzs4GAAQHB4ufY4qLi4NcLpc40u7D+3MREfVuvD8XERERERERERERERGZaK2ZIAiC1FEQEREREREREREREREREREREREREREREREREREREREREREREREREREREREREdGt1dbW4tixY8jIyMDOnTuxf/9+XL9+HUOHDsWIESMQFRUlPoKDg2FmZiZ1yEREfYper4darUZmZqb4OHbsGOrq6mBnZ4exY8ciMTERiYmJiIiIGJBfENzU1ISsrCykp6cjIyMDO3bswOXLl2Fvb4/Q0FCjXBQUFDQgjxERUUe0lXOOHj2K69evw83NDXFxcUhMTMSECRMQEhIidbjUBRqNBjt37sTOnTuxa9cuVFdXw9LSEv7+/kZ5Mzo6GoMHD5Y6XCKiPker1Rrl0yNHjqCiogIymQzh4eFiPo2Li8PQoUOlDlcyVVVV+PXXX8XrisePH0dzczPkcjlCQkIQHBzM64pERN3k2rVryMrKQk5ODrKzs436f66urhg9ejRiYmIG9DVHGji0Wq3YHklPT0dOTg4AQC6XG/WRQ0JCoFKpJI6WiGhgaGhowNmzZ285PjphwgTExMQgNjYWgwYNkjpkyeh0OqSnp2Pnzp3Yvn07ioqKIJPJEBAQYJTHoqKiYG1tLXW4RES91s3XdA8ePIjq6mrY2Nhg/PjxYt6Ji4uDlZWV1OFKhv0nIqLeh/2njjPMlUlPT8fevXtRXFws9p9ajk2OHz8eTk5OUodLRNSvGPpcLccmDf0JlUolzqNJSEiAp6enxNF2XGhoKJKSkrBs2TKpQyHql7KysvDhhx9i7969KCsrAwAoFAr4+Phg4sSJmDx5MsLDw/vsnLIffvgBixYtgkajgV6vN/n1er0ea9euxUcffYT8/Hw0NzdDpVLhd7/7HRYsWNDqGpap5YEb78HSpUuRkZGBpqYmjBkzBm+++SYmTJjQpXguXryIr776Chs3bkRWVhbq6urg4eGB0aNH4y9/+QvCwsKMtm1qeeDG58zef/99rFmzBmfOnIGlpSWioqLwyiuvIDExsVX5mJgYZGRktHms//SnP+G9995rc11n38ekpCRs27YNKSkpePXVV1utN/X96kz8jY2N+OCDD5CamoozZ87A2toaUVFReOaZZzB16lTxf0sQBBw4cADr1q3Dzz//jKKiIgwdOhTDhw/HU089hTlz5rT6P+zs8SSizlGr1QgMDERWVlab58Tevv2O6u9tA4Pb5Yj2mJo7TM2VQMdzR2e3D5iWWzsaT2faEt2ZW02tr6nxr1q1Ck8//fQtj92UKVPw448/dqo8YNrfW2faEj0hJSUFqampUKvVPb5vIiIiIiIiIiLqWVevXsWhQ4fE+YNHjhxBQ0NDm/dLDgwMhEwmkzpkIqIB4ebPch0+fBiVlZXi/G7D/U4SEhLg7OwsdbiS4f25iIikw/tzERERERERERERERHRHbDWQuoIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIjo9mxsbBATE4OYmBi89NJLqKmpwbFjx3Ds2DFkZmZi586dWLlyJZqamuDk5CR+sXBUVBRCQ0OhUqkgk8mkrgYRUa9QU1ODvLw8ZGVliV9mf/LkSdTX18PGxgbh4eGIiorC/PnzERUVhaCgIJ5DAchkMjG3/OlPf0JjYyNOnDiBo0eP4ujRo9i7dy8+/PBD6PV6ODg4IDo6GtHR0YiKikJYWBh8fX15HIlowKmtrUVeXh6OHz8uni9PnjyJhoYGDBkyBJGRkRg3bhwWLlyI6Oho+Pn5SR0y3UEqlQoLFizAggUL0NzcjJycHGRmZor9uG+//RY1NTUYNGgQQkNDxTwbERGBwMBA2NraSl0FIqJeoampCQUFBTh58qTYh8vMzMT58+dhbm6OgIAAREVF4eWXXxbPo3Z2dlKH3Wu4uLhg2rRpmDZtGgDgwoULYj46duwYfvjhB7z//vsQBAGurq6IjIxEZGQkIiIixOuKFhb8yjsiotspLy9HdnY2jh8/Lp5jz549i+bmZjg5OSEyMhIxMTF47rnnEBkZiWHDhkkdMlGPUigUmD59OqZPnw4A0Gq1OHbsGI4fP47jx49j7dq1ePPNNwEA7u7uiIiIEB8jR46ESqWCpaWllFUgIurTtFotcnNzxfPu8ePHcebMGTQ1NeGuu+5CeHg4Ro8ejSeffBKvs21mAAAgAElEQVQREREcH72JXC43ymMajQZHjhwRr/tv3boVV65cgZWVFUaOHCmOkxqOpY2NjcQ1ICLqWU1NTSgsLMSJEyeQmZkpni8vXLgACwsLBAcHIzo6GikpKYiOjkZYWBisrKykDrvXYP+JiEha7D91Tcu5MgCQn58vzpM5fvw4Vq5cKY7zDhs2TBybjIyMRHBwMORyucQ1ICLq/fR6vTiPpuXYZEVFBczMzODn54fIyEj89re/Fc+xTk5OUodNRL1Mc3MzlixZgnfffRdPP/00vvrqKwQEBOD69es4e/YsVq1ahSVLlmDJkiU4cuQIoqOjTd7HtWvXEBERgYCAAKSlpXVDLdqXn5+PxYsXo6ioCBUVFZ3ezu9+9zukpqZiyZIlSEtLg0wmw3/+8x8sXLgQ27dvx7Zt27pU/tChQ0hISEBSUhJyc3NhaWmJV155BfHx8fj+++8xadKkTm//xRdfxNq1a7F8+XKsW7cOQ4YMQWZmJp566ilERUXh66+/RnJycqfLNzU1ITk5Gdu3b8c///lPzJo1C1euXEFKSgomTZqEdevWYebMmZ0+9kDX3sfPP/+81fG+manvl6lqamowZcoUXLlyBe+//z5GjRqFqqoqvPDCC0hKSsKpU6cwYsQIAIBarUZMTAwSExPx9ddfY/jw4dBqtXj11Vcxd+5cZGVlYfny5V2Kh4joVvp726CljuSI9piSOzqTK03JHZ3Zvqm51ZR4TG1LdIYp8Zha3+6If/z48V0qb8rfG9sSREREREREREQkNXt7eyQmJiIxMRHAjWvCWVlZ4jzMffv24d///jcaGxtha2uL0NBQcQ58WFgYAgICMGTIEIlrQUTUd129ehVqtRonTpwQz70nTpxATU0NLC0tERISgoiICLz66quIiIhAeHg478/VAu/PRUTUM3h/LiIiIiIiIiIiIiIi6i5mgiAIUgdBRERERERERERERERERERERERERERERERERERERERERERERERERERERERERERdd+3aNWRlZSEzM1N8qNVqNDU1wdLSEp6enggODkZISIj4MygoCDY2NlKHTkTULS5fvoxz584hOzsbOTk50Gg0yM7ORl5eHpqbm2FlZYVhw4YhKipKfIwePRpWVlZSh95nNTY24syZM2IeysjIQFZWlpiL/P39ERISApVKJeaiESNGYNCgQVKHTkTUJQ0NDTh79ixycnLEvJOdnW3UHvf390dMTAwmTJiAqKgoBAUFwdzcXOrQSUJNTU3Iy8sz6sNlZWWhpqYGACCXy1vlzZEjR8LNzU3iyImIukdjYyNKSkqMcmlOTg5yc3NRW1sL4Ma5sWUfbsKECXB0dJQ48r7v6tWrOHHihJiPcnJycOrUKTQ0NLS6rmjIS2FhYbC3t5c6dCKiHqfVao3ylEajwalTp1BRUQEAcHBwQHBwsFG+Cg4OhpmZmcSRE/V+ly9fxqlTp9psk1hYWMDLy8uoj6xSqdhPJiJqwdCvNoyJGtosp06dwpUrVwC03Vbhteo7Q6vVGo2RHjx4sNW13pZzdtivJqL+oKmpCUVFRa1yz83jXS3HSCMjIzlf8Q5g/4mIqGvYf5JWy/6TIY9pNBoAwNChQzFs2DCjPBYcHIzAwEDIZDKJIyci6lkt56W2zFm3mkczbtw4ODs7Sxz5nRUaGoqkpCQsW7ZM6lCI+pWlS5fib3/7G/7973/jqaeearPMyy+/jLfffhtHjhxBdHS0yfu4evUqwsPDERAQgB9++KGrIZtk9uzZCA0NxZ///Gf4+PigvLwcer3epG1oNBr4+fkhIiICx44dM1o3adIk/Pzzzzh8+DBGjRrVqfLNzc0IDQ3FhQsXkJ+fD2trawA3rrmFhISgtrYWZ8+eFT9vZer2582bB5lMho8++sio7IkTJxAeHg5/f3+cOXNGXG5q+bVr1+KJJ57AwoUL8a9//UtcLggCgoODUVFRAY1Gg7vuuktcFxMTg/fee6/Df0+dfR+1Wi1GjBiBqVOn4osvvkBKSgpeffVVozKmHs/OxP/MM8/gyy+/xJkzZ4yuy9XU1MDJyQlHjx7FiBEjAAB5eXkYOXIkKisr4eDgIJZtaGiAQqHAtWvXcPnyZaPP35kaDxF1jVqtRmBgILKyshAWFtbntn87/b1tYNCRHNEeU3NHZ3KlKbmjM9s3NbeaEo+pbQmge3OrqfU1Nf5Vq1bhp59+wpYtW1pt6+zZsxg5ciQKCwvh7u7eqfKm/r11pi3RE1JSUpCamgq1Wt2j+yUiIiIiIiIiot5Jr9dDrVYbzR88ceIErl27BuC/8zZvvt+Wj48P524SEf2fixcvtrq/ck5ODgoLC9u9x3JUVJQ4L4U6j/fnIiLqON6fi4iIiIiIiIiIiIiIethaC6kjICIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKiO8POzg4xMTGIiYkRl9XU1CA3Nxe5ubnIyclBbm4uvv32WyxfvhxNTU2wsLAQv1g4KCgIAQEB8PPzg0qlgkKhkLA2REQd09DQgKKiImg0GuTn5yMnJwd5eXnIycmBTqcDANja2iIwMBBBQUF47LHHEBgYiJCQEPj5+UEmk0lcg/7F0tISISEhCAkJwW9/+1sAN3KR4Yubc3NzkZ2djY0bN6KwsBCCIMDKykp8f0JCQoxykYODg8Q1IiL6L0EQUFZWhvz8fOTn5yMvL0/8UvqioiIIgoBBgwaJ57RZs2YhKCgII0aMwLBhw5hzqBWZTNYqbzY1NUGj0Yh5Mzc3F5mZmfjyyy9RU1MDAHB3dxf7cMHBwWLe9Pb2hpWVlZRVIiLqEJ1OJ/bh8vLyxJyq0Wig1+shk8ng6+uLkJAQ3HPPPXjuuefE856tra3U4fdL9vb2ra4r1tbWIjc3F2q1Wuxrb9u2DefOnUNDQwPMzMzg5eUltn0CAwOhUqng6+sLb29vWFpaSlgjIqKuKS8vh0ajQUFBAdRqtZivzpw5g+vXrwMAPD09xXPgQw89hMDAQAQHB8PNzU3i6In6rqFDh7bZJmn5f5iXl4e9e/fi448/Rn19PYAb/WTDOKfh+rKvry98fX3ZfiSifkev16OkpAQFBQUoKCjAmTNnkJeXh9zcXBQUFECv18Pc3Bze3t4ICAjA6NGjMXfuXAQEBCA4OBiurq5SV6HfUigUUCgUmDZtGoAb13rPnTtnNEa6b98+fPTRR7h+/TrMzMzg7e0tjpEGBgZi2LBh8PPzg1KphJmZmcQ1IiL6r0uXLoljpGfOnBHPbXl5eeI5zcfHB0FBQRg/fjz+8Ic/ICQkBMHBwbCzs5M6/H6J/Sciottj/6n3urn/BACVlZXIzs6GWq0W58t89tlnKC4uBgAMGjQIw4cPR2BgIAIDAxEQECDmMLlcLlVViIi6rLGxEcXFxdBoNNBoNOIcDbVaLc5LtbS0hL+/P4KCgjB58mQsXrxYPB+yHU9EnZGbm4v/+Z//QVRUFJ566ql2y7388stYsWJFp/djb2+P/Pz8Tr++K/7zn//A2tq6S9soKSkBAAQFBbVaFxgYiJ9//hnFxcUYNWpUp8rv27cP2dnZWLhwoVGsMpkMs2bNwhtvvIG0tDQ8/PDDndr+6tWr26xXWFgYrK2tkZ+fD0EQxPEIU8tv3rwZAIza9QBgZmaGBx54AG+//Ta+/vprzJs3r83tdkRn38f58+dj+vTpiI2NxRdffNFmGVOPp6kqKirw8ccfY8GCBa3mEtna2orX61rus7GxsdV2rKys4OnpiaysLNTX12PQoEGdioeI6FYGQtvAoCM5oj2m5g5Tc6WpuaMzudiU3GpqPKa2JUxlajyAafU1Nf5hw4YhNja2zde8//77SE5Ohru7u7jM1PKm/r2xLUFERERERERERH2BhYVFq3ttNTc3Q6PRiPPfDXNnvvnmG1RXVwO4cZ9lw/z3oKAgDBs2DL6+vvDx8eHn+omoX6qsrERBQQEKCwtx7tw5cU6hWq3GtWvXAABOTk7iPMK4uDjx3ie+vr6832U34f25iIiM8f5cRERERERERERERETUm1hIHQAREREREREREREREREREREREREREREREREREREREREREREREREREREREREREXUfW1tbREdHIzo62mj59evXxS/WzMnJQU5ODtLS0vDPf/5T/ILNwYMHQ6VSQaVSwc/PT3xueAwePFiKKhHRAFRdXQ2NRtPmo6SkBE1NTQAABwcH8cuAp0yZgpCQEAQFBcHb2xtmZmYS12LgsrW1xahRozBq1Cij5TU1NWIeys7ORm5uLtauXYvCwkLxPXV0dBRzkJ+fn/hQqVTw8PDg+0pEd9z169dRWFiI/Px8o4ch79TX1wMAbGxsEBAQgKCgICxYsACBgYEYMWIEVCoVZDKZxLWgvkwmk8Hf3x/+/v5ITk4WlwuCgKKiIuTl5SE7Oxt5eXk4fvw41q9fjwsXLoiv9fDwaNV3M+ROJycnqapFRANMfX09CgoKoNFojPKo4VFXVwcAGDRoEPz9/REUFISZM2ciODgYgYGBCAwMxKBBgySuBdnY2CAqKgpRUVFGy/V6PTQaDXJycpCXl4e8vDxkZGTgs88+w+XLlwEY5yRfX1/4+vqKz1UqFdzc3KSoEhGRqKamRsxVhp8tnxtylZWVFXx9fREcHIypU6ciKChIzFX29vYS14JoYLCxsUFERAQiIiKMljc3N6OwsBBqtRq5ubniz82bN6OiokIs5+rqCh8fH7FN4uvrK/7u7e0NKyurnq4SEdFt6XQ6FBQUGD0KCwtRUFCAkpIS6PV6ADfG4IYPH46AgADMmTMHgYGBCAgIQEBAAKytrSWuBclkMvH9aKmpqQkajUYcH83OzsauXbvw4Ycfora2FsCN+Tq+vr6txkf9/Pzg6+vL6yZEdMcJgoCysjLxeu7N46TV1dUAbpzbvL29ERISgilTpuD5559HcHAwgoKCYGtrK3EtiP0nIhqI2H/qP1xdXeHq6oqEhASj5VevXhVzlyGPbdq0CRqNBg0NDQAAa2trcRyyrfFJOzs7KapERCSqrKw0Gots+bPlZyGGDh2K4cOHIygoCPHx8QgICEBwcDD8/PxgYWEhcS2IqD/5+OOP0dzcjOnTp9+y3F133SXOn+hr7kQ7PzAwEJaWlsjLy2u1Li8vD2ZmZhg5cmSny+/atQsAWn3ut+WyX375BQ8//HCntt+empoa1NXVITQ0tEOfyWqvvOG6kqura6vXyOVyAEB6ejrmzZt32320pzPv46effors7Gxs2LABW7dubbfcnTqe7fnuu+/Q1NSEmJiYTm8DAC5duoSzZ88iIiICQ4cO7dK2iIjaMxDaBkDHc0R7TM0dpuZKU3NHZ3KxKbn1TuUyU9sedzKeO9EmbC/+xMREJCYmtip/9epVrF27Ftu2bTNabmr5O9VWYVuCiIiIiIiIiIh6O3NzcwwbNgzDhg3D1KlTjdadP39evKeJYR7hmjVrUFRUJM4PtbGxMZr/fvM8eF4XI6Le6MqVK63mvrd81NTUAAAsLCzg7e2NgIAAxMfH48knn0RgYCCCgoLg7OwscS0I4P25iKh/4/25iIiIiIiIiIiIiIioL+GdeYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIBqBBgwYhNDQUoaGhRssFQUBZWZn4hZyGx+HDh7FhwwZUVFSIZRUKBVQqFXx8fODh4QGlUgkvLy8olUoolUq4u7v3dLWIqA9qaGiATqdDSUkJSkpKoNVqxeeFhYXQaDS4dOkSgBtfYu/p6QmVSgU/Pz/cc889UKlU4u8ODg4S14ZMYWtr2+YX3jc2NorvfX5+PvLz86HRaLBt2zbk5+ejtrYWwI1cZnjvPT094eHhAU9PT/G5h4cHBg8eLEXViKgXu3TpEkpLS1FcXIzS0lKUlZWhqKgIxcXFyM/PR2lpKZqbmwEALi4u8PPzg0qlwsMPPyw+9/Pzg0KhkLgmNNCYmZnBx8cHPj4+mDJlitG6ixcvturDaTQa7N69G8XFxdDr9QCAoUOHin04Q85UKpVi7lQoFLCyspKiekTUx5SXl6OsrAxlZWUoLi6GVqtFaWmp2I7XarUQBAEA4OrqKvbbHnzwQfG5SqWCUqmEubm5xLUhU1lYWGD48OEYPnx4q3XV1dUoKCiARqNBQUGB+Hzfvn0oLi5GQ0MDAMDGxgYqlQq+vr7w9PSEQqEQ85LhuZ2dXU9XjYj6icbGRpSXl6OkpETMV4brjoZcVVlZKZZ3d3cXz0mGXOXr6yvmKplMJmFtiKg95ubmYrvy3nvvNVpXU1MjtkUMj8LCQvz4448oKCjAlStXxG0oFAr4+vrC29sbCoUCSqVS7CN7enrCzc0NFhb8+l8iunOqq6vF8VCdTidepzb0qwsKClBfXw8AsLS0hJeXF3x9feHn54fExET4+vrC19cXPj4+cHNzk7g21BkymQz+/v7w9/dHcnKy0TqdTmc0Ppqfn49Dhw5h/fr1YhvW3NwcHh4eUKlU8Pb2FufpeHh4wNvbG0qlkuPmRNTK9evXUVpaajROanhuGFcy5B/DtTs/Pz/Exsbi8ccfh5+fH/z8/ODt7c2xpD6I/Sci6qvYfyIAsLe3R3R0NKKjo42WNzc3o7S0tNXY5NGjR7Fp0ybodDqxrKurq/i3oFAo4OXlJeYyT09PyOVyWFpa9nTViKifqKmpEefOGMYldTodiouLxbHJmpoaAP/NVyqVCv7+/pg0aZLR2KSTk5PEtSGigWLfvn0AgLCwMJNfq9fr8c0332D16tU4deoULl++jGHDhmHevHlYuHChOB9wy5YtePDBB8XX1dXVYfDgwa2WFxQU4KWXXsL27dshk8kwbtw4rFixAn5+fl2sZde5ublh+fLleP755/HKK69g8eLFkMlkWL16NXbu3In/9//+n9EcOlPL5+XlAQA8PDxa7VupVAIAzpw50+ntt2fTpk0AgKVLl3boOLRX3tnZGQCMPm9sUFVVBQAoLCxste6LL77AvHnzcPbsWVhYWCA0NBRPP/00Zs+e3aF4bqW0tBQvvPACvvnmG9jb29+ybGePZ0fjP3bsGADAwcEBL7zwAjZu3IiKigrI5XI88MADeOONN+Do6NhufFeuXMGpU6ewZMkSuLu74/PPP2+zXHceTyIaOAZC28CUHNEeU3OHqbnS1NzR2VzcUV3NZQa3a3v0VG7tLFPbTp999hm8vLwQFxfXpfJdbft1tC1BRERERERERETUmzk7OyMmJgYxMTFGy/V6PUpKSsS574b5g5mZmfj666+N5g86OjqKc+AN90w2zH+Xy+Xw8PCAjY1NT1eNiPqxuro6lJaWivPgDXMLS0tLUVRUhMLCQlRXV4vl5XI5fHx84OvriwceeECcA2+4JxM/p9M38f5cRNTb8f5cRERERERERERERETU33B0nYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIRGZmZvDw8ICHhwfi4uJara+pqUF+fj40Go34ZcNFRUU4ffo0tFqt0Rd7WllZQalUQqlUwsvLC0qlEh4eHuIXDiuVSri4uMDKyqonq0hEPaimpgY6nQ5arRbFxcXilwK3fF5eXg5BEADc+KJzuVwOT09PeHh4IDExESqVSnx4eXnB0tJS4lpRd7O0tIS/vz/8/f3bXF9eXo78/HzxUVBQgJycHOzYsQOlpaW4fv26WNbV1VXMay1zkbe3NxQKBeRyOWxsbHqqakTUzaqrq8Uvoi8tLUVpaamYc0pLS1FUVISamhqx/NChQ8Xzw/DhwzFlyhT4+fnBz88PKpUKQ4YMkbA2RB3n4OCAqKgoREVFtVqn1+tRXFws9uE0Gg0KCwuRmZmJzZs3o7y8HI2NjQBu9Afd3Nzg4eFh1I8zPJfL5VAoFLC1te3pKhJRD2lsbERlZaXYXzPk1JbPtVqtUZvbxcVFbGeHhoYiOTnZqB9nZ2cnYY2opzk5OcHJyQnR0dGt1jU1NaG0tBQFBQUoKCgQc1J2djZ+/vlnlJaWoq6uTiw/ZMgQsT+nUCjg5eUFhUIBpVIJT09PuLm5wcXFBTKZrCerSEQSu3z5MnQ6HXQ6ndjvM1x71Gq1ra43ymQyuLm5wdPTEwqFAmPHjsWsWbOgUqng6+sLlUoFa2triWtFRHeara0tRowYgREjRrS5vrq6GgUFBSgsLBTbJiUlJTh9+jR0Oh0qKirEsubm5nB3d4dSqYRCoRDPJ4Y2sFwuh7u7OxwcHHqqekTUS9XV1Yl9akO7pLS0FDqdDsXFxWL7pb6+XnyNra2t0Xll1KhR8PX1FR9KpZJ9ngFGLpdDLpcjJiam1bqrV6+K83UMP4uLi5GZmYnS0lJcunRJLGtrawsvLy+jcdKW133d3Nzg7Ozck1Ujom5UW1trNDfHcE23qKhIfN6yjTto0CCxPevl5YVHHnkEKpVKHCeVy+US1oZ6GvtPRCQF9p/oTjA3N4eXlxe8vLxw9913t1pfV1dnNC5p+MzDwYMH8dVXX6GiogJNTU0AbsyXaZnDbh6XNPTVhg4d2tPVJCIJNTc3o7KyEpWVlSgpKTGaS6PVasWxysuXL4uvGTx4sNEch6SkJKNxSQ8PD+YrIuoVdDodAMDR0dHk1/7000+YOXMm/va3v2Hjxo1oamrC+vXrsWjRIpSWluKdd94BACQnJ0MQBCQnJ2Pr1q3i629evmjRIrz00kv49NNPcfDgQSQlJWHWrFk4fPiw0X4nTpyIEydO4Pvvv8fYsWO7UHvTPPfcc5DL5Xj++efx97//HQDg7OyM1atX4/e//32Xyhuu67c1L9sw7/LixYtdiudmFRUVePnllzFv3jw8+uijXSo/efJkpKWlIS0tDZMmTTJa99NPPwGA0Wc3DC5evIhPP/0UAQEBKC0txV//+lfMmTMHv/76K/71r3/dNqZbmTdvHmbPno2JEyd2qHxnjmdH4zf8n/3+97/Hb37zG+zduxeOjo747rvv8Mwzz2D79u04fPhwm/2MZcuW4bXXXgMAxMfHY/Pmze1ev+vO40lEA8dAaBuYmiPaY0ruMDVXmpo7OpuLO6orucygI22PnsitnWVq20kQBKxcuRKLFi3q0PZvV76zbT9T2hJERERERERERER9kYWFhTh/tC319fXi/EHDXPiioiIcPXoUW7ZsQXl5ORoaGsTyDg4O4vx3w31QW86DN9zbxMLCoqeqSES9UFNTE6qqqlBRUSHOIzTMgzfMKSwrKzOa62FpaQl3d3fxvBIfHw9fX1/4+PiI5zHe72Tg4f25iKi78f5cREREREREREREREQ0EHFmDxERERERERERERERERERERERERERERERERERERERERERERERERERERERERERdZitrS1CQ0MRGhra5vqGhgacP38eOp0OGo0GWq1WfH7gwAHodDoUFRWhqalJfM3gwYOhUCggl8vh4ODQ5nOFQgEPDw9YWVn1VFWJqB11dXW4ePEidDodtFptu891Ol2rL7B3dnYW/68jIyPxyCOPiP/jKpUKXl5esLDgbfTp1tzd3eHu7o4JEya0uf7ixYtG+cfwPC8vD7t27UJRURFqamrE8oMHD75l/jEs8/Lygr29fU9Vk4j+z815p72cU1paioaGBvF1LduYCoUCERERUKlU4u9+fn646667JKwZUc+wsLCASqWCSqVqt8zFixeNcqbhZ25uLn755ReTcmdbz4lIei3byLfqx1VWVhpds3FwcBBzZ1BQECZOnCj23+RyOdvIZBKZTAZvb294e3sjPj6+zTJ1dXVtXlfUarU4deoUtFotysvLIQiC+JqOXFtkn46odzP8798qR128eBGlpaW4cuWK+DorKys4OTmJuWn8+PFG/UCVSgVPT09YWlpKWDsi6o2cnJzg5OSE6OjoNte3HO+8uU1iuM5cUlKCq1evGr3O0H6+XT+ZY55Efcet2ik3/35zX6Vln9rHxwfjx483ukZt+EnUUfb29ggPD0d4eHib6+vr66HVao1ylyF/nTx5Evn5+bh06ZLRa1r+nd5qnFQul8PMzKwnqklE/8fQJr3VnJyWywxazs1RqVSIiYkxuqarUCjg7e0NmUwmYe2oL2H/iYg6iv0n6m2sra0RHByM4ODgdsu0N1/GkMOKi4tx7do1sfygQYPg6OjYoTkzrq6unA9N1EsZ5tDcbl5qVVUV9Hq9+Lqb56ROnTrVaFxSLpfD3d0d5ubmEtaOiKhjWrbH2xIeHo4TJ06Ivz/wwAPYsmWL+Ht8fDyWLFki/r5w4UIcPnwYK1aswGuvvYYhQ4Z0OJZ58+Zh3LhxAIDExETcf//9+Prrr3H+/Hk4OzuL5ZqbmyEIwm1jv5MEQcCTTz6JNWvW4O2338bs2bMhk8nw7bff4tlnn8WOHTuQmpoqtvtMLX+7fQMwujbf1e1XV1djypQpiI+Px6pVq24bw+3Kz5s3D2vWrMGqVasQEBCAmTNnoqamBv/4xz9QVlYG4Ea7vKX09HSj3wMCAvD5559DrVbj/fffx5w5czBmzJjbxtaWTz75BGfPnsU333zTofKdOZ6mxF9fXw/gxjFYs2aNuJ3f/va3KCkpwauvvop3330Xb731VqvYXn31VfzlL3+BRqPBihUrEBERgddffx2vvfaaUbnuPJ5ENLD097aBqTmiPabmDlNzpam5ozO52BRdyWVAx9oePZVbO8PUthMA/Pjjj9DpdJg7d26Xy3el7dfRtgQREREREREREVF/NXjwYAQFBSEoKKjdMjffh6flPPiTJ0926d4mvOcWUd9huMdlR+6vfLs5hffff3+r+e/8DBeZivfnIqL28P5cREREREREREREREREt8a7fREREREREREREREREREREREREREREREREREREREREREREREREdBYzPgAACAASURBVBERERERERER0R1jZWUFhUIBhUKBqKioNstcv34dZWVl0Ol0qKysFL8Mu6KiAjqdDqdPn8bOnTtRXl6O2tpao9c6OTnBzc0NLi4ucHJygpOTE5ydneHo6Cj+bnhu+GlhwVtyE7Wnrq4O1dXVqK6uxoULF1BdXY3z588b/W74WV5eftv/S4VCgZCQECQkJEAul8PV1RXu7u5QKBRwd3eHmZmZRDWlgcTBwQEODg4ICQlpc70gCCgvL0dZWRnKy8vFXFRZWYny8nIUFhbi8OHD0Gq1uHr1aqttu7u7w9XVVcxBzs7ORnnn5gf/7on+6/r160a5xZB3DLmn5TqdTofy8nLU1dWJrzczM4OrqytcXFwgl8vh7u6OsWPHQqFQwNXVFW5ubpDL5fDw8MBdd90lYU2J+hYHBwdERUW124cz5E6tVovy8nJUVVVBp9OhoqICVVVVKC4uRmZmJiorK3H+/Hmj19rY2MDd3R3u7u5t9ttu7tM5OjrCxsamJ6pN1Cfp9XqjPHpzv61lf66yshIVFRWorq422kbL/0sXFxd4eHggKipK7NO5urpCLpdDqVRi0KBBEtWUBipra2uoVCqoVKp2y9TU1KC4uFjMR5WVlUbPT506hR07dqCysrLVNQwHBwe4ubnB1dUVjo6Ora4jGp63XG5tbd3d1SbqN5qamsQ8ZHi097uhLVlVVQW9Xi9uw8LCAi4uLmJecnFxwZgxY8TnhuXu7u5wc3OTsLZE1J91ZLwTgNgGMVxnNox3Gs5xBw4cEJ/X19cbvdbJyUm8zmVof7R8tGyXGB52dnbdXXWifquhocGoTXKrh+H/uaqqCo2NjeI2zM3NxfaIYQw0JCQE8fHxkMvl4jqlUgl3d3dYWlpKWGMaiAYPHnzbPvXly5dRWlpqlL8M46QVFRXIyspCZWUlKisrIQiC+Dpra2vxetLNY6HtjZfyuhLRfwmCIF7TvXms9Obx0qqqKpSXl+PChQtG27Czs4NSqYSLiwvc3d0RFBSEuLg4KBQKuLm5wc3NTcxB5ubmEtWUBiL2n4j6H/afaCC53XwZAKisrIROp2tzXLKqqgonT54Un7c33mGYb9pW3rp5fFImk/VE1Yn6hbq6ujbHJNuaU2Nof97c17K2toabm5s4N9zd3R3h4eFwdXUVf3d1dYWXlxdsbW0lqikR0Z2nVCpRXl7eas6vQVZWFgDg6NGjGDVqlNG6qVOnYurUqa1eExYWhtTUVGRnZ2PcuHEdjuXm7Xt6egIAtFotnJ2dxeV79uzp8DbvlC+++AKffPIJFi5ciMWLF4vLFyxYgPLycrz++usYO3YsFi1a1Knyhs871NTUtNq3YVnLz0SYuv2btzd58mQEBwfj888/v227syPlBw8ejN27d+Ott97C8uXLsXjxYjg5OeGhhx7Cpk2bEBsbC3f3/8/evUfJWdaHA//ObpKd2fsmu7mQoLjQcKsEgppAloswImjDKR49RVs9hx5LW22ptkrbA9YqnJ+2VOulHmyLPaSHcqy2XgoIyIgCCVBsAqF4IKIBueQednf2nmQzvz8473R29pKdTcKQ5PM5Z07e2X3mne++eWe+z+19n4VTvk/ive99bzz22GNxxx13xIoVK6b1mlIvvPBCfPKTn4zvf//7087ZB3M8pxN/Ekc2mx13ffPq1avj+uuvj3vvvTc++9nPTrjPOXPmxCmnnBI333xzbN++Pf7qr/4qzjnnnMhmszOKB2AqR3PdYCY5YjKV5o5Kc2WlueNQ5uKJHEwuq7TuUe5w5NZKzDT+r3zlK/GhD31o2uNUU5U/2LrKwdQlAAAAAOBYcKB7VUZEDA4OFu9tsn379uJ9t5J5hD/72c/iJz/5SWzdunXc/Srr6+uL848mmgM/2cM9K2HmpjsH/pVXXolt27bF9u3bx92XqKmpacx89yVLlsTy5cuL13MlcwuPP/54cwqpGvfngiOb+3MBAAAAAAAcHlYsBwAAAAAAAAAAAAAAAAAAAAAAAAAAAOA1VVdXd8DFhhMDAwOxdevW4oKlW7ZsKS48vHv37njuuefif/7nf2L37t2xe/fuGBgYGLePlpaW4iLDpYsNt7S0REtLSzQ3N0dbW9uY58m2Rbk5EnR3d0c+n4/e3t7o7e0ds93b2xs9PT3R29sbu3btKn5WkgWBh4aGxu2vfGHuefPmRWdnZyxYsKC4oP3ChQtj4cKF0dHREXPmzKnCXw0zl0qlYtGiRbFo0aIDlh0aGort27cXF7tPctKOHTti9+7d8ctf/jIee+yx4mdrcHBw3HuVL3ifPJJcU/4ozUmzZllWgtefoaGhMXk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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -1000,10 +1000,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:40.205857Z",
-     "iopub.status.busy": "2024-06-27T08:57:40.205476Z",
-     "iopub.status.idle": "2024-06-27T08:57:40.209896Z",
-     "shell.execute_reply": "2024-06-27T08:57:40.209370Z"
+     "iopub.execute_input": "2024-06-28T08:59:15.509907Z",
+     "iopub.status.busy": "2024-06-28T08:59:15.509554Z",
+     "iopub.status.idle": "2024-06-28T08:59:15.513983Z",
+     "shell.execute_reply": "2024-06-28T08:59:15.513451Z"
     }
    },
    "outputs": [],
@@ -1038,10 +1038,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:40.211932Z",
-     "iopub.status.busy": "2024-06-27T08:57:40.211544Z",
-     "iopub.status.idle": "2024-06-27T08:57:40.228375Z",
-     "shell.execute_reply": "2024-06-27T08:57:40.227811Z"
+     "iopub.execute_input": "2024-06-28T08:59:15.516049Z",
+     "iopub.status.busy": "2024-06-28T08:59:15.515838Z",
+     "iopub.status.idle": "2024-06-28T08:59:15.532202Z",
+     "shell.execute_reply": "2024-06-28T08:59:15.531627Z"
     }
    },
    "outputs": [
@@ -1055,7 +1055,7 @@
        "  white-space: pre-wrap;\n",
        "}\n",
        "</style>\n",
-       "<small>shape: (50, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>treeID</th><th>visited_nodes</th><th>score</th><th>splits</th></tr><tr><td>i64</td><td>list[i64]</td><td>f64</td><td>str</td></tr></thead><tbody><tr><td>0</td><td>[1, 2, … 7]</td><td>-0.598675</td><td>&quot;[{&#x27;pyName in { AutoUsed48Month…</td></tr><tr><td>1</td><td>[1, 21, 23]</td><td>-0.464293</td><td>&quot;[{&#x27;pyGroup in { DepositAccount…</td></tr><tr><td>2</td><td>[1, 2, … 15]</td><td>-0.402499</td><td>&quot;[{&#x27;pyName in { AutoUsed48Month…</td></tr><tr><td>3</td><td>[1, 11, … 23]</td><td>-0.355028</td><td>&quot;[{&#x27;pyName in { PremierChecking…</td></tr><tr><td>4</td><td>[1, 2, … 32]</td><td>-0.23541</td><td>&quot;[{&#x27;pyName in { AutoUsed48Month…</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>45</td><td>[1, 2, … 14]</td><td>-0.066687</td><td>&quot;[{&#x27;pyName in { AutoUsed48Month…</td></tr><tr><td>46</td><td>[1, 3, … 34]</td><td>-0.150818</td><td>&quot;[{&#x27;IH.SMS.Outbound.NoResponse.…</td></tr><tr><td>47</td><td>[1, 11, … 14]</td><td>0.135699</td><td>&quot;[{&#x27;pyName in { AutoUsed48Month…</td></tr><tr><td>48</td><td>[1, 23, … 49]</td><td>0.153612</td><td>&quot;[{&#x27;pyName in { AutoUsed48Month…</td></tr><tr><td>49</td><td>[1, 2, … 29]</td><td>0.055928</td><td>&quot;[{&#x27;Customer.NetWealth &lt; 18233.…</td></tr></tbody></table></div>"
+       "<small>shape: (50, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>treeID</th><th>visited_nodes</th><th>score</th><th>splits</th></tr><tr><td>i64</td><td>list[i64]</td><td>f64</td><td>str</td></tr></thead><tbody><tr><td>0</td><td>[1, 2, … 6]</td><td>-0.580508</td><td>&quot;[{&#x27;pyName in { AutoUsed48Month…</td></tr><tr><td>1</td><td>[1, 21, 22]</td><td>-0.444396</td><td>&quot;[{&#x27;pyGroup in { DepositAccount…</td></tr><tr><td>2</td><td>[1, 2, … 14]</td><td>-0.382323</td><td>&quot;[{&#x27;pyName in { AutoUsed48Month…</td></tr><tr><td>3</td><td>[1, 11, … 23]</td><td>-0.355028</td><td>&quot;[{&#x27;pyName in { PremierChecking…</td></tr><tr><td>4</td><td>[1, 2, … 7]</td><td>-0.318181</td><td>&quot;[{&#x27;pyName in { AutoUsed48Month…</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>45</td><td>[1, 2, … 24]</td><td>0.257483</td><td>&quot;[{&#x27;pyName in { AutoUsed48Month…</td></tr><tr><td>46</td><td>[1, 3, … 21]</td><td>0.16425</td><td>&quot;[{&#x27;IH.SMS.Outbound.NoResponse.…</td></tr><tr><td>47</td><td>[1, 2, … 10]</td><td>-0.165074</td><td>&quot;[{&#x27;pyName in { AutoUsed48Month…</td></tr><tr><td>48</td><td>[1, 2, … 21]</td><td>0.374687</td><td>&quot;[{&#x27;pyName in { AutoUsed48Month…</td></tr><tr><td>49</td><td>[1, 2, … 19]</td><td>0.012298</td><td>&quot;[{&#x27;Customer.NetWealth &lt; 18233.…</td></tr></tbody></table></div>"
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        "shape: (50, 4)\n",
@@ -1064,17 +1064,17 @@
        "│ ---    ┆ ---           ┆ ---       ┆ ---                             │\n",
        "│ i64    ┆ list[i64]     ┆ f64       ┆ str                             │\n",
        "╞════════╪═══════════════╪═══════════╪═════════════════════════════════╡\n",
-       "│ 0      ┆ [1, 2, … 7]   ┆ -0.598675 ┆ [{'pyName in { AutoUsed48Month… │\n",
-       "│ 1      ┆ [1, 21, 23]   ┆ -0.464293 ┆ [{'pyGroup in { DepositAccount… │\n",
-       "│ 2      ┆ [1, 2, … 15]  ┆ -0.402499 ┆ [{'pyName in { AutoUsed48Month… │\n",
+       "│ 0      ┆ [1, 2, … 6]   ┆ -0.580508 ┆ [{'pyName in { AutoUsed48Month… │\n",
+       "│ 1      ┆ [1, 21, 22]   ┆ -0.444396 ┆ [{'pyGroup in { DepositAccount… │\n",
+       "│ 2      ┆ [1, 2, … 14]  ┆ -0.382323 ┆ [{'pyName in { AutoUsed48Month… │\n",
        "│ 3      ┆ [1, 11, … 23] ┆ -0.355028 ┆ [{'pyName in { PremierChecking… │\n",
-       "│ 4      ┆ [1, 2, … 32]  ┆ -0.23541  ┆ [{'pyName in { AutoUsed48Month… │\n",
+       "│ 4      ┆ [1, 2, … 7]   ┆ -0.318181 ┆ [{'pyName in { AutoUsed48Month… │\n",
        "│ …      ┆ …             ┆ …         ┆ …                               │\n",
-       "│ 45     ┆ [1, 2, … 14]  ┆ -0.066687 ┆ [{'pyName in { AutoUsed48Month… │\n",
-       "│ 46     ┆ [1, 3, … 34]  ┆ -0.150818 ┆ [{'IH.SMS.Outbound.NoResponse.… │\n",
-       "│ 47     ┆ [1, 11, … 14] ┆ 0.135699  ┆ [{'pyName in { AutoUsed48Month… │\n",
-       "│ 48     ┆ [1, 23, … 49] ┆ 0.153612  ┆ [{'pyName in { AutoUsed48Month… │\n",
-       "│ 49     ┆ [1, 2, … 29]  ┆ 0.055928  ┆ [{'Customer.NetWealth < 18233.… │\n",
+       "│ 45     ┆ [1, 2, … 24]  ┆ 0.257483  ┆ [{'pyName in { AutoUsed48Month… │\n",
+       "│ 46     ┆ [1, 3, … 21]  ┆ 0.16425   ┆ [{'IH.SMS.Outbound.NoResponse.… │\n",
+       "│ 47     ┆ [1, 2, … 10]  ┆ -0.165074 ┆ [{'pyName in { AutoUsed48Month… │\n",
+       "│ 48     ┆ [1, 2, … 21]  ┆ 0.374687  ┆ [{'pyName in { AutoUsed48Month… │\n",
+       "│ 49     ┆ [1, 2, … 19]  ┆ 0.012298  ┆ [{'Customer.NetWealth < 18233.… │\n",
        "└────────┴───────────────┴───────────┴─────────────────────────────────┘"
       ]
      },
@@ -1100,17 +1100,17 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:40.230380Z",
-     "iopub.status.busy": "2024-06-27T08:57:40.230067Z",
-     "iopub.status.idle": "2024-06-27T08:57:40.233788Z",
-     "shell.execute_reply": "2024-06-27T08:57:40.233218Z"
+     "iopub.execute_input": "2024-06-28T08:59:15.534177Z",
+     "iopub.status.busy": "2024-06-28T08:59:15.533862Z",
+     "iopub.status.idle": "2024-06-28T08:59:15.537293Z",
+     "shell.execute_reply": "2024-06-28T08:59:15.536785Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.005635052249022455"
+       "0.036858528035468414"
       ]
      },
      "execution_count": 19,
@@ -1135,17 +1135,17 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:40.235704Z",
-     "iopub.status.busy": "2024-06-27T08:57:40.235404Z",
-     "iopub.status.idle": "2024-06-27T08:57:40.251145Z",
-     "shell.execute_reply": "2024-06-27T08:57:40.250583Z"
+     "iopub.execute_input": "2024-06-28T08:59:15.539195Z",
+     "iopub.status.busy": "2024-06-28T08:59:15.538887Z",
+     "iopub.status.idle": "2024-06-28T08:59:15.553496Z",
+     "shell.execute_reply": "2024-06-28T08:59:15.553056Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.005635052249022455"
+       "0.036858528035468414"
       ]
      },
      "execution_count": 20,
@@ -1169,19 +1169,19 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:40.253049Z",
-     "iopub.status.busy": "2024-06-27T08:57:40.252741Z",
-     "iopub.status.idle": "2024-06-27T08:57:40.292564Z",
-     "shell.execute_reply": "2024-06-27T08:57:40.292115Z"
+     "iopub.execute_input": "2024-06-28T08:59:15.555370Z",
+     "iopub.status.busy": "2024-06-28T08:59:15.555206Z",
+     "iopub.status.idle": "2024-06-28T08:59:15.594168Z",
+     "shell.execute_reply": "2024-06-28T08:59:15.593705Z"
     }
    },
    "outputs": [
     {
      "data": {
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+     "iopub.status.busy": "2024-06-28T08:59:17.964755Z",
+     "iopub.status.idle": "2024-06-28T08:59:19.437367Z",
+     "shell.execute_reply": "2024-06-28T08:59:19.436839Z"
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    "outputs": [],
@@ -88,10 +88,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:53.496715Z",
-     "iopub.status.busy": "2024-06-27T08:57:53.496431Z",
-     "iopub.status.idle": "2024-06-27T08:57:53.503799Z",
-     "shell.execute_reply": "2024-06-27T08:57:53.503331Z"
+     "iopub.execute_input": "2024-06-28T08:59:19.439954Z",
+     "iopub.status.busy": "2024-06-28T08:59:19.439469Z",
+     "iopub.status.idle": "2024-06-28T08:59:19.446275Z",
+     "shell.execute_reply": "2024-06-28T08:59:19.445787Z"
     }
    },
    "outputs": [
@@ -151,10 +151,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:53.505827Z",
-     "iopub.status.busy": "2024-06-27T08:57:53.505488Z",
-     "iopub.status.idle": "2024-06-27T08:57:53.574923Z",
-     "shell.execute_reply": "2024-06-27T08:57:53.574335Z"
+     "iopub.execute_input": "2024-06-28T08:59:19.448401Z",
+     "iopub.status.busy": "2024-06-28T08:59:19.448070Z",
+     "iopub.status.idle": "2024-06-28T08:59:19.516866Z",
+     "shell.execute_reply": "2024-06-28T08:59:19.516389Z"
     }
    },
    "outputs": [
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@@ -294,19 +294,19 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:53.607471Z",
-     "iopub.status.busy": "2024-06-27T08:57:53.607143Z",
-     "iopub.status.idle": "2024-06-27T08:57:53.635931Z",
-     "shell.execute_reply": "2024-06-27T08:57:53.635481Z"
+     "iopub.execute_input": "2024-06-28T08:59:19.548606Z",
+     "iopub.status.busy": "2024-06-28T08:59:19.548265Z",
+     "iopub.status.idle": "2024-06-28T08:59:19.577019Z",
+     "shell.execute_reply": "2024-06-28T08:59:19.576575Z"
     }
    },
    "outputs": [
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+       "<div>                            <div id=\"e2ec512c-6a9e-44db-8bfe-c003b5abd202\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"e2ec512c-6a9e-44db-8bfe-c003b5abd202\")) {                    Plotly.newPlot(                        \"e2ec512c-6a9e-44db-8bfe-c003b5abd202\",                        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(0.0%) at (50,0)\\u003c\\u002fsup\\u003e\\u003cbr\\u003e\\u003csup\\u003eQuery: {'ModelID': ['68e1d164-81e3-5da0-816c-0bbc3c20ac6c', '277a2c48-8888-5b71-911e-443d52c9b50f', '0ad9da35-f171-5ab9-973f-1cc4b89b4e8f', '08ca1302-9fc0-57bf-9031-d4179d400493', '838ca9ec-74ea-5a54-8c57-f45295116921', '34f7d1b9-92f7-57dc-ab62-0de35dda0221', '26dda9e0-34a2-5e0a-8ee2-fd0c39de729e', 'a75db3a2-fecc-5cc8-b75f-0a44447f9cca', 'a8cde9d1-c720-537c-8fb8-8c59506bddfe', 'cb67c091-5a81-56f4-9845-71afa9c8076e', '081d0acf-bfc3-5c0a-abc4-29106252efb8', 'dd502275-e98b-51f7-8935-c30c84641871', '84107ad6-c998-5377-b0d0-e02be2a8d768', '8e40882a-aacf-5f03-9be8-f833c7932da2', '708be173-0f46-55ee-add6-6fdca447a70a', 'c3f445b2-6037-5560-9f16-71a7259d2c2c', 'a87d51fb-cfdc-598d-b84a-dcdd64e1e750', '692f453a-f825-5a90-ab40-f83cbb34b058', 'f3afaa9b-d0d5-5be9-a3b9-9fdfc4847411', '602303a4-5243-523d-8964-473f3ea1200b', '30cc2c18-5852-581a-abaa-555bf46a3b9c', '320c4d01-43a5-510e-8fa8-1fc1bb4216da', 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        "                            \n",
-       "var gd = document.getElementById('b455a706-ecac-4ffa-852d-838697abbb46');\n",
+       "var gd = document.getElementById('e2ec512c-6a9e-44db-8bfe-c003b5abd202');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -360,19 +360,19 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:53.638020Z",
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@@ -417,19 +417,19 @@
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        "                            \n",
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@@ -532,19 +532,19 @@
    "execution_count": 10,
    "metadata": {
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-     "iopub.execute_input": "2024-06-27T08:57:54.030552Z",
-     "iopub.status.busy": "2024-06-27T08:57:54.030372Z",
-     "iopub.status.idle": "2024-06-27T08:57:54.104155Z",
-     "shell.execute_reply": "2024-06-27T08:57:54.103587Z"
+     "iopub.execute_input": "2024-06-28T08:59:19.977287Z",
+     "iopub.status.busy": "2024-06-28T08:59:19.976936Z",
+     "iopub.status.idle": "2024-06-28T08:59:20.051544Z",
+     "shell.execute_reply": "2024-06-28T08:59:20.051082Z"
     }
    },
    "outputs": [
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@@ -589,19 +589,19 @@
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@@ -711,19 +711,19 @@
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@@ -846,19 +846,19 @@
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mbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Name\"},\"dtick\":1},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"autorange\":\"reversed\",\"dtick\":1,\"automargin\":true},\"coloraxis\":{\"colorscale\":[[0,\"#d91c29\"],[0.01,\"#F76923\"],[0.3,\"#20aa50\"],[0.8,\"#20aa50\"],[1,\"#0000FF\"]],\"cmin\":50,\"cmax\":100},\"title\":{\"text\":\"Top predictors over all models \\u003cbr\\u003e\\u003csup\\u003eQuery: col(\\\"PredictorName\\\").is_in([Series])\\u003c\\u002fsup\\u003e\"}},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('92567559-6c34-4ed5-92e2-8a281e0296ae');\n",
+       "var gd = document.getElementById('758b43c3-e8c2-43aa-842a-2c31c5327c82');\n",
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diff --git a/Python/articles/Example_Data_Anonymization.html b/Python/articles/Example_Data_Anonymization.html
index 45ef9355..94deb714 100644
--- a/Python/articles/Example_Data_Anonymization.html
+++ b/Python/articles/Example_Data_Anonymization.html
@@ -456,7 +456,7 @@ <h2>Input data<a class="headerlink" href="#Input-data" title="Link to this headi
   white-space: pre-wrap;
 }
 </style>
-<small>shape: (7, 7)</small><table border="1" class="dataframe"><thead><tr><th>PREDICTOR_0</th><th>filename</th><th>PREDICTOR_2</th><th>PREDICTOR_3</th><th>Context_Name</th><th>IH_PREDICTOR_0</th><th>Decision_Outcome</th></tr><tr><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>bool</td></tr></thead><tbody><tr><td>1.2167e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;6147846389658722553&quot;</td><td>&quot;2288875718529266449&quot;</td><td>&quot;4249663721192810638&quot;</td><td>&quot;10229436525961356759&quot;</td><td>false</td></tr><tr><td>8.4508e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;9546562836500446922&quot;</td><td>&quot;1384965645898912072&quot;</td><td>&quot;4249663721192810638&quot;</td><td>&quot;17317120373498427018&quot;</td><td>true</td></tr><tr><td>1.7675e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;1421966280809475974&quot;</td><td>&quot;17904252642100228159&quot;</td><td>&quot;2440746720503869809&quot;</td><td>&quot;10229436525961356759&quot;</td><td>false</td></tr><tr><td>9.0321e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;9546562836500446922&quot;</td><td>&quot;13125856642762912113&quot;</td><td>&quot;14674378938643070016&quot;</td><td>&quot;10229436525961356759&quot;</td><td>false</td></tr><tr><td>2.3087e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;6147846389658722553&quot;</td><td>&quot;4452067448264920949&quot;</td><td>&quot;14674378938643070016&quot;</td><td>&quot;15987834086564834453&quot;</td><td>true</td></tr><tr><td>1.4344e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;1421966280809475974&quot;</td><td>&quot;11369660024816883368&quot;</td><td>&quot;15823670710176640254&quot;</td><td>null</td><td>false</td></tr><tr><td>1.6626e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;1421966280809475974&quot;</td><td>&quot;16332345803428636889&quot;</td><td>&quot;14674378938643070016&quot;</td><td>&quot;15987834086564834453&quot;</td><td>false</td></tr></tbody></table></div></div>
+<small>shape: (7, 7)</small><table border="1" class="dataframe"><thead><tr><th>PREDICTOR_0</th><th>PREDICTOR_1</th><th>filename</th><th>PREDICTOR_3</th><th>Context_Name</th><th>IH_PREDICTOR_0</th><th>Decision_Outcome</th></tr><tr><td>str</td><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>bool</td></tr></thead><tbody><tr><td>&quot;12966594121660586144&quot;</td><td>8.1394e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;16213733806433532114&quot;</td><td>&quot;12389604203113505438&quot;</td><td>&quot;7189849604827994599&quot;</td><td>false</td></tr><tr><td>&quot;17909105717745695898&quot;</td><td>3.2558e17</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;7368088096525236362&quot;</td><td>&quot;12389604203113505438&quot;</td><td>&quot;10411723875908756696&quot;</td><td>true</td></tr><tr><td>&quot;2224057289082822490&quot;</td><td>1.4181e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;5336126776549509997&quot;</td><td>&quot;4185235921171887873&quot;</td><td>&quot;7189849604827994599&quot;</td><td>false</td></tr><tr><td>&quot;1197588711120825259&quot;</td><td>1.7915e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;7368088096525236362&quot;</td><td>&quot;15348917492779909101&quot;</td><td>&quot;7189849604827994599&quot;</td><td>false</td></tr><tr><td>&quot;14652648524834149339&quot;</td><td>1.2550e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;16213733806433532114&quot;</td><td>&quot;15348917492779909101&quot;</td><td>&quot;7887436098792540635&quot;</td><td>true</td></tr><tr><td>&quot;10180930787455143344&quot;</td><td>1.9392e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;5336126776549509997&quot;</td><td>&quot;12695467595088885982&quot;</td><td>null</td><td>false</td></tr><tr><td>&quot;11614391923100326336&quot;</td><td>1.5737e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;5336126776549509997&quot;</td><td>&quot;15348917492779909101&quot;</td><td>&quot;7887436098792540635&quot;</td><td>false</td></tr></tbody></table></div></div>
 </div>
 <p>To trace back the columns to their original names, the class also contains a mapping, which does not have to be provided.</p>
 <div class="nbinput docutils container">
@@ -473,10 +473,10 @@ <h2>Input data<a class="headerlink" href="#Input-data" title="Link to this headi
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-{&#39;Customer_CLV&#39;: &#39;PREDICTOR_0&#39;,
+{&#39;Customer_City&#39;: &#39;PREDICTOR_0&#39;,
+ &#39;Customer_CLV&#39;: &#39;PREDICTOR_1&#39;,
  &#39;filename&#39;: &#39;filename&#39;,
- &#39;Customer_MaritalStatus&#39;: &#39;PREDICTOR_2&#39;,
- &#39;Customer_City&#39;: &#39;PREDICTOR_3&#39;,
+ &#39;Customer_MaritalStatus&#39;: &#39;PREDICTOR_3&#39;,
  &#39;Context_Name&#39;: &#39;Context_Name&#39;,
  &#39;IH_Web_Inbound_Accepted_pxLastGroupID&#39;: &#39;IH_PREDICTOR_0&#39;,
  &#39;Decision_Outcome&#39;: &#39;Decision_Outcome&#39;}
@@ -554,7 +554,7 @@ <h2>Configs<a class="headerlink" href="#Configs" title="Link to this heading">¶
   white-space: pre-wrap;
 }
 </style>
-<small>shape: (7, 7)</small><table border="1" class="dataframe"><thead><tr><th>PREDICTOR_0</th><th>filename</th><th>PREDICTOR_2</th><th>PREDICTOR_3</th><th>Context_Name</th><th>IH_Web_Inbound_Accepted_pxLastGroupID</th><th>Decision_Outcome</th></tr><tr><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>3.7144e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;5116113255984028597&quot;</td><td>&quot;9718179972840610916&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;3904756066300791155&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>1.7416e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;11206299812145901282&quot;</td><td>&quot;2606546173167921635&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;2073517796186034520&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>7.3266e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;18231480856777751062&quot;</td><td>&quot;15698682252462766581&quot;</td><td>&quot;MoneyMarketSavingsAccount&quot;</td><td>&quot;3904756066300791155&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>4.8024e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;11206299812145901282&quot;</td><td>&quot;16203385141368777126&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;3904756066300791155&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>9.8712e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;5116113255984028597&quot;</td><td>&quot;6952337591127786287&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;10099839356039493843&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>3.7920e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;18231480856777751062&quot;</td><td>&quot;16041653561048533847&quot;</td><td>&quot;UPlusFinPersonal&quot;</td><td>null</td><td>&quot;Rejected&quot;</td></tr><tr><td>1.3435e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;18231480856777751062&quot;</td><td>&quot;7277846800018900085&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;10099839356039493843&quot;</td><td>&quot;Rejected&quot;</td></tr></tbody></table></div></div>
+<small>shape: (7, 7)</small><table border="1" class="dataframe"><thead><tr><th>PREDICTOR_0</th><th>PREDICTOR_1</th><th>filename</th><th>PREDICTOR_3</th><th>Context_Name</th><th>IH_Web_Inbound_Accepted_pxLastGroupID</th><th>Decision_Outcome</th></tr><tr><td>str</td><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;2693063016261329210&quot;</td><td>8.5424e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;2176186410263664439&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;2480916800669711861&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;13663209729075354870&quot;</td><td>1.1573e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;6206444605637468390&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;6148724870095980507&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>&quot;13752064661150663353&quot;</td><td>1.7837e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;13742469919383404989&quot;</td><td>&quot;MoneyMarketSavingsAccount&quot;</td><td>&quot;2480916800669711861&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;4278909319353159294&quot;</td><td>2.1048e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;6206444605637468390&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;2480916800669711861&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;5289473295907448034&quot;</td><td>7.0451e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;2176186410263664439&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;7992382043670570244&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>&quot;14984668176722395866&quot;</td><td>1.0940e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;13742469919383404989&quot;</td><td>&quot;UPlusFinPersonal&quot;</td><td>null</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;8020568563095088654&quot;</td><td>1.6966e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;13742469919383404989&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;7992382043670570244&quot;</td><td>&quot;Rejected&quot;</td></tr></tbody></table></div></div>
 </div>
 <p>The configs can also be written and read as such:</p>
 <div class="nbinput nblast docutils container">
@@ -586,7 +586,7 @@ <h2>Configs<a class="headerlink" href="#Configs" title="Link to this heading">¶
   white-space: pre-wrap;
 }
 </style>
-<small>shape: (7, 7)</small><table border="1" class="dataframe"><thead><tr><th>PREDICTOR_0</th><th>filename</th><th>PREDICTOR_2</th><th>PREDICTOR_3</th><th>Context_Name</th><th>IH_Web_Inbound_Accepted_pxLastGroupID</th><th>Decision_Outcome</th></tr><tr><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>1.2499e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;18293990344771377923&quot;</td><td>&quot;10401712909067898287&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;7470891602762162258&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>4.4825e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;6519604743693388226&quot;</td><td>&quot;17427030756996613094&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;17303967803918605159&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>1.7559e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;12013569918084110708&quot;</td><td>&quot;2116059138085243262&quot;</td><td>&quot;MoneyMarketSavingsAccount&quot;</td><td>&quot;7470891602762162258&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>1.8016e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;6519604743693388226&quot;</td><td>&quot;16761861177852596977&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;7470891602762162258&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>1.6517e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;18293990344771377923&quot;</td><td>&quot;12818238716068238964&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;4600961781277298053&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>1.3564e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;12013569918084110708&quot;</td><td>&quot;3822978993054429388&quot;</td><td>&quot;UPlusFinPersonal&quot;</td><td>null</td><td>&quot;Rejected&quot;</td></tr><tr><td>6.3226e17</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;12013569918084110708&quot;</td><td>&quot;12414514550878942568&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;4600961781277298053&quot;</td><td>&quot;Rejected&quot;</td></tr></tbody></table></div></div>
+<small>shape: (7, 7)</small><table border="1" class="dataframe"><thead><tr><th>PREDICTOR_0</th><th>PREDICTOR_1</th><th>filename</th><th>PREDICTOR_3</th><th>Context_Name</th><th>IH_Web_Inbound_Accepted_pxLastGroupID</th><th>Decision_Outcome</th></tr><tr><td>str</td><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;15817344893893706607&quot;</td><td>6.4529e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;5428569341997076199&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;8077201405311366912&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;9455517170590553341&quot;</td><td>7.2162e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;16361791012203088208&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;3563248967714237740&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>&quot;2757873603826598519&quot;</td><td>1.7725e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;12998887436935034178&quot;</td><td>&quot;MoneyMarketSavingsAccount&quot;</td><td>&quot;8077201405311366912&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;2769473506539838498&quot;</td><td>4.8717e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;16361791012203088208&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;8077201405311366912&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;14307498553172057419&quot;</td><td>5.5270e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;5428569341997076199&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;14638340152789677911&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>&quot;12104382896446018419&quot;</td><td>1.7970e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;12998887436935034178&quot;</td><td>&quot;UPlusFinPersonal&quot;</td><td>null</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;7909492205048073676&quot;</td><td>1.8294e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;12998887436935034178&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;14638340152789677911&quot;</td><td>&quot;Rejected&quot;</td></tr></tbody></table></div></div>
 </div>
 </section>
 <section id="Exporting">
@@ -608,10 +608,10 @@ <h2>Exporting<a class="headerlink" href="#Exporting" title="Link to this heading
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Customer_CLV=PREDICTOR_0
+Customer_City=PREDICTOR_0
+Customer_CLV=PREDICTOR_1
 filename=filename
-Customer_MaritalStatus=PREDICTOR_2
-Customer_City=PREDICTOR_3
+Customer_MaritalStatus=PREDICTOR_3
 Context_Name=Context_Name
 IH_Web_Inbound_Accepted_pxLastGroupID=IH_Web_Inbound_Accepted_pxLastGroupID
 Decision_Outcome=Decision_Outcome
@@ -646,7 +646,7 @@ <h2>Exporting<a class="headerlink" href="#Exporting" title="Link to this heading
   white-space: pre-wrap;
 }
 </style>
-<small>shape: (7, 6)</small><table border="1" class="dataframe"><thead><tr><th>PREDICTOR_0</th><th>PREDICTOR_2</th><th>PREDICTOR_3</th><th>Context_Name</th><th>IH_Web_Inbound_Accepted_pxLastGroupID</th><th>Decision_Outcome</th></tr><tr><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>7.0882e17</td><td>&quot;16422462981718732615&quot;</td><td>&quot;7626264414049753285&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;9571052652269174784&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>1.6083e19</td><td>&quot;974077714294797116&quot;</td><td>&quot;11390115286477205611&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;15926504249917894361&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>7.7979e18</td><td>&quot;576385218094076406&quot;</td><td>&quot;18364511084597139830&quot;</td><td>&quot;MoneyMarketSavingsAccount&quot;</td><td>&quot;9571052652269174784&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>1.6801e19</td><td>&quot;974077714294797116&quot;</td><td>&quot;13544611003525504311&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;9571052652269174784&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>4.6573e17</td><td>&quot;16422462981718732615&quot;</td><td>&quot;2847659491701829113&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;4292160008144934078&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>1.7649e19</td><td>&quot;576385218094076406&quot;</td><td>&quot;6698486850386849814&quot;</td><td>&quot;UPlusFinPersonal&quot;</td><td>null</td><td>&quot;Rejected&quot;</td></tr><tr><td>1.2700e19</td><td>&quot;576385218094076406&quot;</td><td>&quot;12350877057713676354&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;4292160008144934078&quot;</td><td>&quot;Rejected&quot;</td></tr></tbody></table></div></div>
+<small>shape: (7, 6)</small><table border="1" class="dataframe"><thead><tr><th>PREDICTOR_0</th><th>PREDICTOR_1</th><th>PREDICTOR_3</th><th>Context_Name</th><th>IH_Web_Inbound_Accepted_pxLastGroupID</th><th>Decision_Outcome</th></tr><tr><td>str</td><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;11607069941383855728&quot;</td><td>6.0221e18</td><td>&quot;6488086644001720326&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;3813394123237880986&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;18278964075147118547&quot;</td><td>2.2228e18</td><td>&quot;6077337721412885530&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;3359379319836418694&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>&quot;14791683637853151924&quot;</td><td>6.8988e18</td><td>&quot;4239025813693951965&quot;</td><td>&quot;MoneyMarketSavingsAccount&quot;</td><td>&quot;3813394123237880986&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;16287708547505018726&quot;</td><td>3.2804e18</td><td>&quot;6077337721412885530&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;3813394123237880986&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;8177611666367621678&quot;</td><td>5.4996e18</td><td>&quot;6488086644001720326&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;18378854800664855497&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>&quot;15369738107837082215&quot;</td><td>9.6513e18</td><td>&quot;4239025813693951965&quot;</td><td>&quot;UPlusFinPersonal&quot;</td><td>null</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;13418639920439002120&quot;</td><td>1.3739e19</td><td>&quot;4239025813693951965&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;18378854800664855497&quot;</td><td>&quot;Rejected&quot;</td></tr></tbody></table></div></div>
 </div>
 </section>
 </section>
diff --git a/Python/articles/Example_Data_Anonymization.ipynb b/Python/articles/Example_Data_Anonymization.ipynb
index 0ede5b7f..e444e681 100644
--- a/Python/articles/Example_Data_Anonymization.ipynb
+++ b/Python/articles/Example_Data_Anonymization.ipynb
@@ -19,10 +19,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:56.255622Z",
-     "iopub.status.busy": "2024-06-27T08:57:56.255459Z",
-     "iopub.status.idle": "2024-06-27T08:57:56.326947Z",
-     "shell.execute_reply": "2024-06-27T08:57:56.326423Z"
+     "iopub.execute_input": "2024-06-28T08:59:22.360419Z",
+     "iopub.status.busy": "2024-06-28T08:59:22.360246Z",
+     "iopub.status.idle": "2024-06-28T08:59:22.432663Z",
+     "shell.execute_reply": "2024-06-28T08:59:22.432104Z"
     },
     "nbsphinx": "hidden"
    },
@@ -45,10 +45,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:56.329246Z",
-     "iopub.status.busy": "2024-06-27T08:57:56.328780Z",
-     "iopub.status.idle": "2024-06-27T08:57:57.405300Z",
-     "shell.execute_reply": "2024-06-27T08:57:57.404795Z"
+     "iopub.execute_input": "2024-06-28T08:59:22.434619Z",
+     "iopub.status.busy": "2024-06-28T08:59:22.434445Z",
+     "iopub.status.idle": "2024-06-28T08:59:23.498132Z",
+     "shell.execute_reply": "2024-06-28T08:59:23.497633Z"
     }
    },
    "outputs": [],
@@ -73,10 +73,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:57.407943Z",
-     "iopub.status.busy": "2024-06-27T08:57:57.407495Z",
-     "iopub.status.idle": "2024-06-27T08:57:57.415984Z",
-     "shell.execute_reply": "2024-06-27T08:57:57.415429Z"
+     "iopub.execute_input": "2024-06-28T08:59:23.500622Z",
+     "iopub.status.busy": "2024-06-28T08:59:23.500208Z",
+     "iopub.status.idle": "2024-06-28T08:59:23.508161Z",
+     "shell.execute_reply": "2024-06-28T08:59:23.507627Z"
     }
    },
    "outputs": [
@@ -139,10 +139,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:57.418149Z",
-     "iopub.status.busy": "2024-06-27T08:57:57.417848Z",
-     "iopub.status.idle": "2024-06-27T08:57:57.427436Z",
-     "shell.execute_reply": "2024-06-27T08:57:57.426888Z"
+     "iopub.execute_input": "2024-06-28T08:59:23.510400Z",
+     "iopub.status.busy": "2024-06-28T08:59:23.510002Z",
+     "iopub.status.idle": "2024-06-28T08:59:23.519507Z",
+     "shell.execute_reply": "2024-06-28T08:59:23.519086Z"
     }
    },
    "outputs": [],
@@ -169,10 +169,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:57.429680Z",
-     "iopub.status.busy": "2024-06-27T08:57:57.429352Z",
-     "iopub.status.idle": "2024-06-27T08:57:57.434588Z",
-     "shell.execute_reply": "2024-06-27T08:57:57.434120Z"
+     "iopub.execute_input": "2024-06-28T08:59:23.521602Z",
+     "iopub.status.busy": "2024-06-28T08:59:23.521265Z",
+     "iopub.status.idle": "2024-06-28T08:59:23.526263Z",
+     "shell.execute_reply": "2024-06-28T08:59:23.525801Z"
     }
    },
    "outputs": [
@@ -186,38 +186,38 @@
        "  white-space: pre-wrap;\n",
        "}\n",
        "</style>\n",
-       "<small>shape: (7, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PREDICTOR_0</th><th>filename</th><th>PREDICTOR_2</th><th>PREDICTOR_3</th><th>Context_Name</th><th>IH_PREDICTOR_0</th><th>Decision_Outcome</th></tr><tr><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>bool</td></tr></thead><tbody><tr><td>1.2167e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;6147846389658722553&quot;</td><td>&quot;2288875718529266449&quot;</td><td>&quot;4249663721192810638&quot;</td><td>&quot;10229436525961356759&quot;</td><td>false</td></tr><tr><td>8.4508e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;9546562836500446922&quot;</td><td>&quot;1384965645898912072&quot;</td><td>&quot;4249663721192810638&quot;</td><td>&quot;17317120373498427018&quot;</td><td>true</td></tr><tr><td>1.7675e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;1421966280809475974&quot;</td><td>&quot;17904252642100228159&quot;</td><td>&quot;2440746720503869809&quot;</td><td>&quot;10229436525961356759&quot;</td><td>false</td></tr><tr><td>9.0321e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;9546562836500446922&quot;</td><td>&quot;13125856642762912113&quot;</td><td>&quot;14674378938643070016&quot;</td><td>&quot;10229436525961356759&quot;</td><td>false</td></tr><tr><td>2.3087e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;6147846389658722553&quot;</td><td>&quot;4452067448264920949&quot;</td><td>&quot;14674378938643070016&quot;</td><td>&quot;15987834086564834453&quot;</td><td>true</td></tr><tr><td>1.4344e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;1421966280809475974&quot;</td><td>&quot;11369660024816883368&quot;</td><td>&quot;15823670710176640254&quot;</td><td>null</td><td>false</td></tr><tr><td>1.6626e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;1421966280809475974&quot;</td><td>&quot;16332345803428636889&quot;</td><td>&quot;14674378938643070016&quot;</td><td>&quot;15987834086564834453&quot;</td><td>false</td></tr></tbody></table></div>"
+       "<small>shape: (7, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PREDICTOR_0</th><th>PREDICTOR_1</th><th>filename</th><th>PREDICTOR_3</th><th>Context_Name</th><th>IH_PREDICTOR_0</th><th>Decision_Outcome</th></tr><tr><td>str</td><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>bool</td></tr></thead><tbody><tr><td>&quot;12966594121660586144&quot;</td><td>8.1394e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;16213733806433532114&quot;</td><td>&quot;12389604203113505438&quot;</td><td>&quot;7189849604827994599&quot;</td><td>false</td></tr><tr><td>&quot;17909105717745695898&quot;</td><td>3.2558e17</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;7368088096525236362&quot;</td><td>&quot;12389604203113505438&quot;</td><td>&quot;10411723875908756696&quot;</td><td>true</td></tr><tr><td>&quot;2224057289082822490&quot;</td><td>1.4181e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;5336126776549509997&quot;</td><td>&quot;4185235921171887873&quot;</td><td>&quot;7189849604827994599&quot;</td><td>false</td></tr><tr><td>&quot;1197588711120825259&quot;</td><td>1.7915e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;7368088096525236362&quot;</td><td>&quot;15348917492779909101&quot;</td><td>&quot;7189849604827994599&quot;</td><td>false</td></tr><tr><td>&quot;14652648524834149339&quot;</td><td>1.2550e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;16213733806433532114&quot;</td><td>&quot;15348917492779909101&quot;</td><td>&quot;7887436098792540635&quot;</td><td>true</td></tr><tr><td>&quot;10180930787455143344&quot;</td><td>1.9392e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;5336126776549509997&quot;</td><td>&quot;12695467595088885982&quot;</td><td>null</td><td>false</td></tr><tr><td>&quot;11614391923100326336&quot;</td><td>1.5737e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;5336126776549509997&quot;</td><td>&quot;15348917492779909101&quot;</td><td>&quot;7887436098792540635&quot;</td><td>false</td></tr></tbody></table></div>"
       ],
       "text/plain": [
        "shape: (7, 7)\n",
-       "┌─────────────┬──────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┐\n",
-       "│ PREDICTOR_0 ┆ filename     ┆ PREDICTOR_2 ┆ PREDICTOR_3 ┆ Context_Nam ┆ IH_PREDICTO ┆ Decision_Ou │\n",
-       "│ ---         ┆ ---          ┆ ---         ┆ ---         ┆ e           ┆ R_0         ┆ tcome       │\n",
-       "│ f64         ┆ str          ┆ str         ┆ str         ┆ ---         ┆ ---         ┆ ---         │\n",
-       "│             ┆              ┆             ┆             ┆ str         ┆ str         ┆ bool        │\n",
-       "╞═════════════╪══════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
-       "│ 1.2167e18   ┆ ../../../../ ┆ 61478463896 ┆ 22888757185 ┆ 42496637211 ┆ 10229436525 ┆ false       │\n",
-       "│             ┆ data/SampleH ┆ 58722553    ┆ 29266449    ┆ 92810638    ┆ 961356759   ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "│ 8.4508e18   ┆ ../../../../ ┆ 95465628365 ┆ 13849656458 ┆ 42496637211 ┆ 17317120373 ┆ true        │\n",
-       "│             ┆ data/SampleH ┆ 00446922    ┆ 98912072    ┆ 92810638    ┆ 498427018   ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "│ 1.7675e19   ┆ ../../../../ ┆ 14219662808 ┆ 17904252642 ┆ 24407467205 ┆ 10229436525 ┆ false       │\n",
-       "│             ┆ data/SampleH ┆ 09475974    ┆ 100228159   ┆ 03869809    ┆ 961356759   ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "│ 9.0321e18   ┆ ../../../../ ┆ 95465628365 ┆ 13125856642 ┆ 14674378938 ┆ 10229436525 ┆ false       │\n",
-       "│             ┆ data/SampleH ┆ 00446922    ┆ 762912113   ┆ 643070016   ┆ 961356759   ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "│ 2.3087e18   ┆ ../../../../ ┆ 61478463896 ┆ 44520674482 ┆ 14674378938 ┆ 15987834086 ┆ true        │\n",
-       "│             ┆ data/SampleH ┆ 58722553    ┆ 64920949    ┆ 643070016   ┆ 564834453   ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "│ 1.4344e19   ┆ ../../../../ ┆ 14219662808 ┆ 11369660024 ┆ 15823670710 ┆ null        ┆ false       │\n",
-       "│             ┆ data/SampleH ┆ 09475974    ┆ 816883368   ┆ 176640254   ┆             ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "│ 1.6626e19   ┆ ../../../../ ┆ 14219662808 ┆ 16332345803 ┆ 14674378938 ┆ 15987834086 ┆ false       │\n",
-       "│             ┆ data/SampleH ┆ 09475974    ┆ 428636889   ┆ 643070016   ┆ 564834453   ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "└─────────────┴──────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘"
+       "┌──────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┐\n",
+       "│ PREDICTOR_0  ┆ PREDICTOR_1 ┆ filename    ┆ PREDICTOR_3 ┆ Context_Nam ┆ IH_PREDICTO ┆ Decision_Ou │\n",
+       "│ ---          ┆ ---         ┆ ---         ┆ ---         ┆ e           ┆ R_0         ┆ tcome       │\n",
+       "│ str          ┆ f64         ┆ str         ┆ str         ┆ ---         ┆ ---         ┆ ---         │\n",
+       "│              ┆             ┆             ┆             ┆ str         ┆ str         ┆ bool        │\n",
+       "╞══════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
+       "│ 129665941216 ┆ 8.1394e18   ┆ ../../../.. ┆ 16213733806 ┆ 12389604203 ┆ 71898496048 ┆ false       │\n",
+       "│ 60586144     ┆             ┆ /data/Sampl ┆ 433532114   ┆ 113505438   ┆ 27994599    ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "│ 179091057177 ┆ 3.2558e17   ┆ ../../../.. ┆ 73680880965 ┆ 12389604203 ┆ 10411723875 ┆ true        │\n",
+       "│ 45695898     ┆             ┆ /data/Sampl ┆ 25236362    ┆ 113505438   ┆ 908756696   ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "│ 222405728908 ┆ 1.4181e19   ┆ ../../../.. ┆ 53361267765 ┆ 41852359211 ┆ 71898496048 ┆ false       │\n",
+       "│ 2822490      ┆             ┆ /data/Sampl ┆ 49509997    ┆ 71887873    ┆ 27994599    ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "│ 119758871112 ┆ 1.7915e19   ┆ ../../../.. ┆ 73680880965 ┆ 15348917492 ┆ 71898496048 ┆ false       │\n",
+       "│ 0825259      ┆             ┆ /data/Sampl ┆ 25236362    ┆ 779909101   ┆ 27994599    ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "│ 146526485248 ┆ 1.2550e19   ┆ ../../../.. ┆ 16213733806 ┆ 15348917492 ┆ 78874360987 ┆ true        │\n",
+       "│ 34149339     ┆             ┆ /data/Sampl ┆ 433532114   ┆ 779909101   ┆ 92540635    ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "│ 101809307874 ┆ 1.9392e18   ┆ ../../../.. ┆ 53361267765 ┆ 12695467595 ┆ null        ┆ false       │\n",
+       "│ 55143344     ┆             ┆ /data/Sampl ┆ 49509997    ┆ 088885982   ┆             ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "│ 116143919231 ┆ 1.5737e19   ┆ ../../../.. ┆ 53361267765 ┆ 15348917492 ┆ 78874360987 ┆ false       │\n",
+       "│ 00326336     ┆             ┆ /data/Sampl ┆ 49509997    ┆ 779909101   ┆ 92540635    ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "└──────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘"
       ]
      },
      "execution_count": 5,
@@ -242,20 +242,20 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:57.436615Z",
-     "iopub.status.busy": "2024-06-27T08:57:57.436282Z",
-     "iopub.status.idle": "2024-06-27T08:57:57.440008Z",
-     "shell.execute_reply": "2024-06-27T08:57:57.439554Z"
+     "iopub.execute_input": "2024-06-28T08:59:23.528152Z",
+     "iopub.status.busy": "2024-06-28T08:59:23.527983Z",
+     "iopub.status.idle": "2024-06-28T08:59:23.531535Z",
+     "shell.execute_reply": "2024-06-28T08:59:23.531096Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'Customer_CLV': 'PREDICTOR_0',\n",
+       "{'Customer_City': 'PREDICTOR_0',\n",
+       " 'Customer_CLV': 'PREDICTOR_1',\n",
        " 'filename': 'filename',\n",
-       " 'Customer_MaritalStatus': 'PREDICTOR_2',\n",
-       " 'Customer_City': 'PREDICTOR_3',\n",
+       " 'Customer_MaritalStatus': 'PREDICTOR_3',\n",
        " 'Context_Name': 'Context_Name',\n",
        " 'IH_Web_Inbound_Accepted_pxLastGroupID': 'IH_PREDICTOR_0',\n",
        " 'Decision_Outcome': 'Decision_Outcome'}"
@@ -285,10 +285,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:57.441903Z",
-     "iopub.status.busy": "2024-06-27T08:57:57.441587Z",
-     "iopub.status.idle": "2024-06-27T08:57:57.445697Z",
-     "shell.execute_reply": "2024-06-27T08:57:57.445270Z"
+     "iopub.execute_input": "2024-06-28T08:59:23.533557Z",
+     "iopub.status.busy": "2024-06-28T08:59:23.533245Z",
+     "iopub.status.idle": "2024-06-28T08:59:23.537386Z",
+     "shell.execute_reply": "2024-06-28T08:59:23.536945Z"
     }
    },
    "outputs": [
@@ -347,10 +347,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:57.447730Z",
-     "iopub.status.busy": "2024-06-27T08:57:57.447357Z",
-     "iopub.status.idle": "2024-06-27T08:57:57.455149Z",
-     "shell.execute_reply": "2024-06-27T08:57:57.454703Z"
+     "iopub.execute_input": "2024-06-28T08:59:23.539346Z",
+     "iopub.status.busy": "2024-06-28T08:59:23.539048Z",
+     "iopub.status.idle": "2024-06-28T08:59:23.546865Z",
+     "shell.execute_reply": "2024-06-28T08:59:23.546331Z"
     }
    },
    "outputs": [
@@ -364,39 +364,39 @@
        "  white-space: pre-wrap;\n",
        "}\n",
        "</style>\n",
-       "<small>shape: (7, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PREDICTOR_0</th><th>filename</th><th>PREDICTOR_2</th><th>PREDICTOR_3</th><th>Context_Name</th><th>IH_Web_Inbound_Accepted_pxLastGroupID</th><th>Decision_Outcome</th></tr><tr><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>3.7144e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;5116113255984028597&quot;</td><td>&quot;9718179972840610916&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;3904756066300791155&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>1.7416e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;11206299812145901282&quot;</td><td>&quot;2606546173167921635&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;2073517796186034520&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>7.3266e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;18231480856777751062&quot;</td><td>&quot;15698682252462766581&quot;</td><td>&quot;MoneyMarketSavingsAccount&quot;</td><td>&quot;3904756066300791155&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>4.8024e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;11206299812145901282&quot;</td><td>&quot;16203385141368777126&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;3904756066300791155&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>9.8712e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;5116113255984028597&quot;</td><td>&quot;6952337591127786287&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;10099839356039493843&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>3.7920e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;18231480856777751062&quot;</td><td>&quot;16041653561048533847&quot;</td><td>&quot;UPlusFinPersonal&quot;</td><td>null</td><td>&quot;Rejected&quot;</td></tr><tr><td>1.3435e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;18231480856777751062&quot;</td><td>&quot;7277846800018900085&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;10099839356039493843&quot;</td><td>&quot;Rejected&quot;</td></tr></tbody></table></div>"
+       "<small>shape: (7, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PREDICTOR_0</th><th>PREDICTOR_1</th><th>filename</th><th>PREDICTOR_3</th><th>Context_Name</th><th>IH_Web_Inbound_Accepted_pxLastGroupID</th><th>Decision_Outcome</th></tr><tr><td>str</td><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;2693063016261329210&quot;</td><td>8.5424e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;2176186410263664439&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;2480916800669711861&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;13663209729075354870&quot;</td><td>1.1573e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;6206444605637468390&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;6148724870095980507&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>&quot;13752064661150663353&quot;</td><td>1.7837e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;13742469919383404989&quot;</td><td>&quot;MoneyMarketSavingsAccount&quot;</td><td>&quot;2480916800669711861&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;4278909319353159294&quot;</td><td>2.1048e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;6206444605637468390&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;2480916800669711861&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;5289473295907448034&quot;</td><td>7.0451e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;2176186410263664439&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;7992382043670570244&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>&quot;14984668176722395866&quot;</td><td>1.0940e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;13742469919383404989&quot;</td><td>&quot;UPlusFinPersonal&quot;</td><td>null</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;8020568563095088654&quot;</td><td>1.6966e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;13742469919383404989&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;7992382043670570244&quot;</td><td>&quot;Rejected&quot;</td></tr></tbody></table></div>"
       ],
       "text/plain": [
        "shape: (7, 7)\n",
-       "┌─────────────┬──────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┐\n",
-       "│ PREDICTOR_0 ┆ filename     ┆ PREDICTOR_2 ┆ PREDICTOR_3 ┆ Context_Nam ┆ IH_Web_Inbo ┆ Decision_Ou │\n",
-       "│ ---         ┆ ---          ┆ ---         ┆ ---         ┆ e           ┆ und_Accepte ┆ tcome       │\n",
-       "│ f64         ┆ str          ┆ str         ┆ str         ┆ ---         ┆ d_pxLast…   ┆ ---         │\n",
-       "│             ┆              ┆             ┆             ┆ str         ┆ ---         ┆ str         │\n",
-       "│             ┆              ┆             ┆             ┆             ┆ str         ┆             │\n",
-       "╞═════════════╪══════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
-       "│ 3.7144e18   ┆ ../../../../ ┆ 51161132559 ┆ 97181799728 ┆ FirstMortga ┆ 39047560663 ┆ Rejected    │\n",
-       "│             ┆ data/SampleH ┆ 84028597    ┆ 40610916    ┆ ge30yr      ┆ 00791155    ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "│ 1.7416e19   ┆ ../../../../ ┆ 11206299812 ┆ 26065461731 ┆ FirstMortga ┆ 20735177961 ┆ Accepted    │\n",
-       "│             ┆ data/SampleH ┆ 145901282   ┆ 67921635    ┆ ge30yr      ┆ 86034520    ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "│ 7.3266e18   ┆ ../../../../ ┆ 18231480856 ┆ 15698682252 ┆ MoneyMarket ┆ 39047560663 ┆ Rejected    │\n",
-       "│             ┆ data/SampleH ┆ 777751062   ┆ 462766581   ┆ SavingsAcco ┆ 00791155    ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆ unt         ┆             ┆             │\n",
-       "│ 4.8024e18   ┆ ../../../../ ┆ 11206299812 ┆ 16203385141 ┆ BasicChecki ┆ 39047560663 ┆ Rejected    │\n",
-       "│             ┆ data/SampleH ┆ 145901282   ┆ 368777126   ┆ ng          ┆ 00791155    ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "│ 9.8712e18   ┆ ../../../../ ┆ 51161132559 ┆ 69523375911 ┆ BasicChecki ┆ 10099839356 ┆ Accepted    │\n",
-       "│             ┆ data/SampleH ┆ 84028597    ┆ 27786287    ┆ ng          ┆ 039493843   ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "│ 3.7920e18   ┆ ../../../../ ┆ 18231480856 ┆ 16041653561 ┆ UPlusFinPer ┆ null        ┆ Rejected    │\n",
-       "│             ┆ data/SampleH ┆ 777751062   ┆ 048533847   ┆ sonal       ┆             ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "│ 1.3435e19   ┆ ../../../../ ┆ 18231480856 ┆ 72778468000 ┆ BasicChecki ┆ 10099839356 ┆ Rejected    │\n",
-       "│             ┆ data/SampleH ┆ 777751062   ┆ 18900085    ┆ ng          ┆ 039493843   ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "└─────────────┴──────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘"
+       "┌──────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┐\n",
+       "│ PREDICTOR_0  ┆ PREDICTOR_1 ┆ filename    ┆ PREDICTOR_3 ┆ Context_Nam ┆ IH_Web_Inbo ┆ Decision_Ou │\n",
+       "│ ---          ┆ ---         ┆ ---         ┆ ---         ┆ e           ┆ und_Accepte ┆ tcome       │\n",
+       "│ str          ┆ f64         ┆ str         ┆ str         ┆ ---         ┆ d_pxLast…   ┆ ---         │\n",
+       "│              ┆             ┆             ┆             ┆ str         ┆ ---         ┆ str         │\n",
+       "│              ┆             ┆             ┆             ┆             ┆ str         ┆             │\n",
+       "╞══════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
+       "│ 269306301626 ┆ 8.5424e18   ┆ ../../../.. ┆ 21761864102 ┆ FirstMortga ┆ 24809168006 ┆ Rejected    │\n",
+       "│ 1329210      ┆             ┆ /data/Sampl ┆ 63664439    ┆ ge30yr      ┆ 69711861    ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "│ 136632097290 ┆ 1.1573e19   ┆ ../../../.. ┆ 62064446056 ┆ FirstMortga ┆ 61487248700 ┆ Accepted    │\n",
+       "│ 75354870     ┆             ┆ /data/Sampl ┆ 37468390    ┆ ge30yr      ┆ 95980507    ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "│ 137520646611 ┆ 1.7837e19   ┆ ../../../.. ┆ 13742469919 ┆ MoneyMarket ┆ 24809168006 ┆ Rejected    │\n",
+       "│ 50663353     ┆             ┆ /data/Sampl ┆ 383404989   ┆ SavingsAcco ┆ 69711861    ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆ unt         ┆             ┆             │\n",
+       "│ 427890931935 ┆ 2.1048e18   ┆ ../../../.. ┆ 62064446056 ┆ BasicChecki ┆ 24809168006 ┆ Rejected    │\n",
+       "│ 3159294      ┆             ┆ /data/Sampl ┆ 37468390    ┆ ng          ┆ 69711861    ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "│ 528947329590 ┆ 7.0451e18   ┆ ../../../.. ┆ 21761864102 ┆ BasicChecki ┆ 79923820436 ┆ Accepted    │\n",
+       "│ 7448034      ┆             ┆ /data/Sampl ┆ 63664439    ┆ ng          ┆ 70570244    ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "│ 149846681767 ┆ 1.0940e19   ┆ ../../../.. ┆ 13742469919 ┆ UPlusFinPer ┆ null        ┆ Rejected    │\n",
+       "│ 22395866     ┆             ┆ /data/Sampl ┆ 383404989   ┆ sonal       ┆             ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "│ 802056856309 ┆ 1.6966e19   ┆ ../../../.. ┆ 13742469919 ┆ BasicChecki ┆ 79923820436 ┆ Rejected    │\n",
+       "│ 5088654      ┆             ┆ /data/Sampl ┆ 383404989   ┆ ng          ┆ 70570244    ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "└──────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘"
       ]
      },
      "execution_count": 8,
@@ -428,10 +428,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:57.457000Z",
-     "iopub.status.busy": "2024-06-27T08:57:57.456719Z",
-     "iopub.status.idle": "2024-06-27T08:57:57.459471Z",
-     "shell.execute_reply": "2024-06-27T08:57:57.459048Z"
+     "iopub.execute_input": "2024-06-28T08:59:23.548957Z",
+     "iopub.status.busy": "2024-06-28T08:59:23.548526Z",
+     "iopub.status.idle": "2024-06-28T08:59:23.551459Z",
+     "shell.execute_reply": "2024-06-28T08:59:23.550952Z"
     }
    },
    "outputs": [],
@@ -444,10 +444,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:57.461447Z",
-     "iopub.status.busy": "2024-06-27T08:57:57.461143Z",
-     "iopub.status.idle": "2024-06-27T08:57:57.468684Z",
-     "shell.execute_reply": "2024-06-27T08:57:57.468266Z"
+     "iopub.execute_input": "2024-06-28T08:59:23.553526Z",
+     "iopub.status.busy": "2024-06-28T08:59:23.553207Z",
+     "iopub.status.idle": "2024-06-28T08:59:23.560686Z",
+     "shell.execute_reply": "2024-06-28T08:59:23.560143Z"
     }
    },
    "outputs": [
@@ -461,39 +461,39 @@
        "  white-space: pre-wrap;\n",
        "}\n",
        "</style>\n",
-       "<small>shape: (7, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PREDICTOR_0</th><th>filename</th><th>PREDICTOR_2</th><th>PREDICTOR_3</th><th>Context_Name</th><th>IH_Web_Inbound_Accepted_pxLastGroupID</th><th>Decision_Outcome</th></tr><tr><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>1.2499e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;18293990344771377923&quot;</td><td>&quot;10401712909067898287&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;7470891602762162258&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>4.4825e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;6519604743693388226&quot;</td><td>&quot;17427030756996613094&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;17303967803918605159&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>1.7559e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;12013569918084110708&quot;</td><td>&quot;2116059138085243262&quot;</td><td>&quot;MoneyMarketSavingsAccount&quot;</td><td>&quot;7470891602762162258&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>1.8016e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;6519604743693388226&quot;</td><td>&quot;16761861177852596977&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;7470891602762162258&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>1.6517e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;18293990344771377923&quot;</td><td>&quot;12818238716068238964&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;4600961781277298053&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>1.3564e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;12013569918084110708&quot;</td><td>&quot;3822978993054429388&quot;</td><td>&quot;UPlusFinPersonal&quot;</td><td>null</td><td>&quot;Rejected&quot;</td></tr><tr><td>6.3226e17</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;12013569918084110708&quot;</td><td>&quot;12414514550878942568&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;4600961781277298053&quot;</td><td>&quot;Rejected&quot;</td></tr></tbody></table></div>"
+       "<small>shape: (7, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PREDICTOR_0</th><th>PREDICTOR_1</th><th>filename</th><th>PREDICTOR_3</th><th>Context_Name</th><th>IH_Web_Inbound_Accepted_pxLastGroupID</th><th>Decision_Outcome</th></tr><tr><td>str</td><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;15817344893893706607&quot;</td><td>6.4529e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;5428569341997076199&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;8077201405311366912&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;9455517170590553341&quot;</td><td>7.2162e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;16361791012203088208&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;3563248967714237740&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>&quot;2757873603826598519&quot;</td><td>1.7725e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;12998887436935034178&quot;</td><td>&quot;MoneyMarketSavingsAccount&quot;</td><td>&quot;8077201405311366912&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;2769473506539838498&quot;</td><td>4.8717e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;16361791012203088208&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;8077201405311366912&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;14307498553172057419&quot;</td><td>5.5270e18</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;5428569341997076199&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;14638340152789677911&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>&quot;12104382896446018419&quot;</td><td>1.7970e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;12998887436935034178&quot;</td><td>&quot;UPlusFinPersonal&quot;</td><td>null</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;7909492205048073676&quot;</td><td>1.8294e19</td><td>&quot;../../../../data/SampleHDS.jso…</td><td>&quot;12998887436935034178&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;14638340152789677911&quot;</td><td>&quot;Rejected&quot;</td></tr></tbody></table></div>"
       ],
       "text/plain": [
        "shape: (7, 7)\n",
-       "┌─────────────┬──────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┐\n",
-       "│ PREDICTOR_0 ┆ filename     ┆ PREDICTOR_2 ┆ PREDICTOR_3 ┆ Context_Nam ┆ IH_Web_Inbo ┆ Decision_Ou │\n",
-       "│ ---         ┆ ---          ┆ ---         ┆ ---         ┆ e           ┆ und_Accepte ┆ tcome       │\n",
-       "│ f64         ┆ str          ┆ str         ┆ str         ┆ ---         ┆ d_pxLast…   ┆ ---         │\n",
-       "│             ┆              ┆             ┆             ┆ str         ┆ ---         ┆ str         │\n",
-       "│             ┆              ┆             ┆             ┆             ┆ str         ┆             │\n",
-       "╞═════════════╪══════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
-       "│ 1.2499e19   ┆ ../../../../ ┆ 18293990344 ┆ 10401712909 ┆ FirstMortga ┆ 74708916027 ┆ Rejected    │\n",
-       "│             ┆ data/SampleH ┆ 771377923   ┆ 067898287   ┆ ge30yr      ┆ 62162258    ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "│ 4.4825e18   ┆ ../../../../ ┆ 65196047436 ┆ 17427030756 ┆ FirstMortga ┆ 17303967803 ┆ Accepted    │\n",
-       "│             ┆ data/SampleH ┆ 93388226    ┆ 996613094   ┆ ge30yr      ┆ 918605159   ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "│ 1.7559e19   ┆ ../../../../ ┆ 12013569918 ┆ 21160591380 ┆ MoneyMarket ┆ 74708916027 ┆ Rejected    │\n",
-       "│             ┆ data/SampleH ┆ 084110708   ┆ 85243262    ┆ SavingsAcco ┆ 62162258    ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆ unt         ┆             ┆             │\n",
-       "│ 1.8016e18   ┆ ../../../../ ┆ 65196047436 ┆ 16761861177 ┆ BasicChecki ┆ 74708916027 ┆ Rejected    │\n",
-       "│             ┆ data/SampleH ┆ 93388226    ┆ 852596977   ┆ ng          ┆ 62162258    ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "│ 1.6517e19   ┆ ../../../../ ┆ 18293990344 ┆ 12818238716 ┆ BasicChecki ┆ 46009617812 ┆ Accepted    │\n",
-       "│             ┆ data/SampleH ┆ 771377923   ┆ 068238964   ┆ ng          ┆ 77298053    ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "│ 1.3564e19   ┆ ../../../../ ┆ 12013569918 ┆ 38229789930 ┆ UPlusFinPer ┆ null        ┆ Rejected    │\n",
-       "│             ┆ data/SampleH ┆ 084110708   ┆ 54429388    ┆ sonal       ┆             ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "│ 6.3226e17   ┆ ../../../../ ┆ 12013569918 ┆ 12414514550 ┆ BasicChecki ┆ 46009617812 ┆ Rejected    │\n",
-       "│             ┆ data/SampleH ┆ 084110708   ┆ 878942568   ┆ ng          ┆ 77298053    ┆             │\n",
-       "│             ┆ DS.jso…      ┆             ┆             ┆             ┆             ┆             │\n",
-       "└─────────────┴──────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘"
+       "┌──────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┐\n",
+       "│ PREDICTOR_0  ┆ PREDICTOR_1 ┆ filename    ┆ PREDICTOR_3 ┆ Context_Nam ┆ IH_Web_Inbo ┆ Decision_Ou │\n",
+       "│ ---          ┆ ---         ┆ ---         ┆ ---         ┆ e           ┆ und_Accepte ┆ tcome       │\n",
+       "│ str          ┆ f64         ┆ str         ┆ str         ┆ ---         ┆ d_pxLast…   ┆ ---         │\n",
+       "│              ┆             ┆             ┆             ┆ str         ┆ ---         ┆ str         │\n",
+       "│              ┆             ┆             ┆             ┆             ┆ str         ┆             │\n",
+       "╞══════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
+       "│ 158173448938 ┆ 6.4529e18   ┆ ../../../.. ┆ 54285693419 ┆ FirstMortga ┆ 80772014053 ┆ Rejected    │\n",
+       "│ 93706607     ┆             ┆ /data/Sampl ┆ 97076199    ┆ ge30yr      ┆ 11366912    ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "│ 945551717059 ┆ 7.2162e18   ┆ ../../../.. ┆ 16361791012 ┆ FirstMortga ┆ 35632489677 ┆ Accepted    │\n",
+       "│ 0553341      ┆             ┆ /data/Sampl ┆ 203088208   ┆ ge30yr      ┆ 14237740    ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "│ 275787360382 ┆ 1.7725e19   ┆ ../../../.. ┆ 12998887436 ┆ MoneyMarket ┆ 80772014053 ┆ Rejected    │\n",
+       "│ 6598519      ┆             ┆ /data/Sampl ┆ 935034178   ┆ SavingsAcco ┆ 11366912    ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆ unt         ┆             ┆             │\n",
+       "│ 276947350653 ┆ 4.8717e18   ┆ ../../../.. ┆ 16361791012 ┆ BasicChecki ┆ 80772014053 ┆ Rejected    │\n",
+       "│ 9838498      ┆             ┆ /data/Sampl ┆ 203088208   ┆ ng          ┆ 11366912    ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "│ 143074985531 ┆ 5.5270e18   ┆ ../../../.. ┆ 54285693419 ┆ BasicChecki ┆ 14638340152 ┆ Accepted    │\n",
+       "│ 72057419     ┆             ┆ /data/Sampl ┆ 97076199    ┆ ng          ┆ 789677911   ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "│ 121043828964 ┆ 1.7970e19   ┆ ../../../.. ┆ 12998887436 ┆ UPlusFinPer ┆ null        ┆ Rejected    │\n",
+       "│ 46018419     ┆             ┆ /data/Sampl ┆ 935034178   ┆ sonal       ┆             ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "│ 790949220504 ┆ 1.8294e19   ┆ ../../../.. ┆ 12998887436 ┆ BasicChecki ┆ 14638340152 ┆ Rejected    │\n",
+       "│ 8073676      ┆             ┆ /data/Sampl ┆ 935034178   ┆ ng          ┆ 789677911   ┆             │\n",
+       "│              ┆             ┆ eHDS.jso…   ┆             ┆             ┆             ┆             │\n",
+       "└──────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘"
       ]
      },
      "execution_count": 10,
@@ -524,10 +524,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:57.470722Z",
-     "iopub.status.busy": "2024-06-27T08:57:57.470415Z",
-     "iopub.status.idle": "2024-06-27T08:57:57.473539Z",
-     "shell.execute_reply": "2024-06-27T08:57:57.473130Z"
+     "iopub.execute_input": "2024-06-28T08:59:23.562686Z",
+     "iopub.status.busy": "2024-06-28T08:59:23.562286Z",
+     "iopub.status.idle": "2024-06-28T08:59:23.565642Z",
+     "shell.execute_reply": "2024-06-28T08:59:23.565136Z"
     }
    },
    "outputs": [
@@ -535,10 +535,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Customer_CLV=PREDICTOR_0\n",
+      "Customer_City=PREDICTOR_0\n",
+      "Customer_CLV=PREDICTOR_1\n",
       "filename=filename\n",
-      "Customer_MaritalStatus=PREDICTOR_2\n",
-      "Customer_City=PREDICTOR_3\n",
+      "Customer_MaritalStatus=PREDICTOR_3\n",
       "Context_Name=Context_Name\n",
       "IH_Web_Inbound_Accepted_pxLastGroupID=IH_Web_Inbound_Accepted_pxLastGroupID\n",
       "Decision_Outcome=Decision_Outcome\n",
@@ -557,10 +557,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:57.475579Z",
-     "iopub.status.busy": "2024-06-27T08:57:57.475259Z",
-     "iopub.status.idle": "2024-06-27T08:57:57.478578Z",
-     "shell.execute_reply": "2024-06-27T08:57:57.478114Z"
+     "iopub.execute_input": "2024-06-28T08:59:23.567484Z",
+     "iopub.status.busy": "2024-06-28T08:59:23.567202Z",
+     "iopub.status.idle": "2024-06-28T08:59:23.570558Z",
+     "shell.execute_reply": "2024-06-28T08:59:23.570045Z"
     }
    },
    "outputs": [],
@@ -573,10 +573,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:57.480529Z",
-     "iopub.status.busy": "2024-06-27T08:57:57.480222Z",
-     "iopub.status.idle": "2024-06-27T08:57:57.484944Z",
-     "shell.execute_reply": "2024-06-27T08:57:57.484404Z"
+     "iopub.execute_input": "2024-06-28T08:59:23.572678Z",
+     "iopub.status.busy": "2024-06-28T08:59:23.572265Z",
+     "iopub.status.idle": "2024-06-28T08:59:23.576560Z",
+     "shell.execute_reply": "2024-06-28T08:59:23.576119Z"
     }
    },
    "outputs": [
@@ -590,32 +590,32 @@
        "  white-space: pre-wrap;\n",
        "}\n",
        "</style>\n",
-       "<small>shape: (7, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PREDICTOR_0</th><th>PREDICTOR_2</th><th>PREDICTOR_3</th><th>Context_Name</th><th>IH_Web_Inbound_Accepted_pxLastGroupID</th><th>Decision_Outcome</th></tr><tr><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>7.0882e17</td><td>&quot;16422462981718732615&quot;</td><td>&quot;7626264414049753285&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;9571052652269174784&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>1.6083e19</td><td>&quot;974077714294797116&quot;</td><td>&quot;11390115286477205611&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;15926504249917894361&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>7.7979e18</td><td>&quot;576385218094076406&quot;</td><td>&quot;18364511084597139830&quot;</td><td>&quot;MoneyMarketSavingsAccount&quot;</td><td>&quot;9571052652269174784&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>1.6801e19</td><td>&quot;974077714294797116&quot;</td><td>&quot;13544611003525504311&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;9571052652269174784&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>4.6573e17</td><td>&quot;16422462981718732615&quot;</td><td>&quot;2847659491701829113&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;4292160008144934078&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>1.7649e19</td><td>&quot;576385218094076406&quot;</td><td>&quot;6698486850386849814&quot;</td><td>&quot;UPlusFinPersonal&quot;</td><td>null</td><td>&quot;Rejected&quot;</td></tr><tr><td>1.2700e19</td><td>&quot;576385218094076406&quot;</td><td>&quot;12350877057713676354&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;4292160008144934078&quot;</td><td>&quot;Rejected&quot;</td></tr></tbody></table></div>"
+       "<small>shape: (7, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PREDICTOR_0</th><th>PREDICTOR_1</th><th>PREDICTOR_3</th><th>Context_Name</th><th>IH_Web_Inbound_Accepted_pxLastGroupID</th><th>Decision_Outcome</th></tr><tr><td>str</td><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;11607069941383855728&quot;</td><td>6.0221e18</td><td>&quot;6488086644001720326&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;3813394123237880986&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;18278964075147118547&quot;</td><td>2.2228e18</td><td>&quot;6077337721412885530&quot;</td><td>&quot;FirstMortgage30yr&quot;</td><td>&quot;3359379319836418694&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>&quot;14791683637853151924&quot;</td><td>6.8988e18</td><td>&quot;4239025813693951965&quot;</td><td>&quot;MoneyMarketSavingsAccount&quot;</td><td>&quot;3813394123237880986&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;16287708547505018726&quot;</td><td>3.2804e18</td><td>&quot;6077337721412885530&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;3813394123237880986&quot;</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;8177611666367621678&quot;</td><td>5.4996e18</td><td>&quot;6488086644001720326&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;18378854800664855497&quot;</td><td>&quot;Accepted&quot;</td></tr><tr><td>&quot;15369738107837082215&quot;</td><td>9.6513e18</td><td>&quot;4239025813693951965&quot;</td><td>&quot;UPlusFinPersonal&quot;</td><td>null</td><td>&quot;Rejected&quot;</td></tr><tr><td>&quot;13418639920439002120&quot;</td><td>1.3739e19</td><td>&quot;4239025813693951965&quot;</td><td>&quot;BasicChecking&quot;</td><td>&quot;18378854800664855497&quot;</td><td>&quot;Rejected&quot;</td></tr></tbody></table></div>"
       ],
       "text/plain": [
        "shape: (7, 6)\n",
-       "┌─────────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┐\n",
-       "│ PREDICTOR_0 ┆ PREDICTOR_2    ┆ PREDICTOR_3    ┆ Context_Name   ┆ IH_Web_Inbound ┆ Decision_Outco │\n",
-       "│ ---         ┆ ---            ┆ ---            ┆ ---            ┆ _Accepted_pxLa ┆ me             │\n",
-       "│ f64         ┆ str            ┆ str            ┆ str            ┆ st…            ┆ ---            │\n",
-       "│             ┆                ┆                ┆                ┆ ---            ┆ str            │\n",
-       "│             ┆                ┆                ┆                ┆ str            ┆                │\n",
-       "╞═════════════╪════════════════╪════════════════╪════════════════╪════════════════╪════════════════╡\n",
-       "│ 7.0882e17   ┆ 16422462981718 ┆ 76262644140497 ┆ FirstMortgage3 ┆ 95710526522691 ┆ Rejected       │\n",
-       "│             ┆ 732615         ┆ 53285          ┆ 0yr            ┆ 74784          ┆                │\n",
-       "│ 1.6083e19   ┆ 97407771429479 ┆ 11390115286477 ┆ FirstMortgage3 ┆ 15926504249917 ┆ Accepted       │\n",
-       "│             ┆ 7116           ┆ 205611         ┆ 0yr            ┆ 894361         ┆                │\n",
-       "│ 7.7979e18   ┆ 57638521809407 ┆ 18364511084597 ┆ MoneyMarketSav ┆ 95710526522691 ┆ Rejected       │\n",
-       "│             ┆ 6406           ┆ 139830         ┆ ingsAccount    ┆ 74784          ┆                │\n",
-       "│ 1.6801e19   ┆ 97407771429479 ┆ 13544611003525 ┆ BasicChecking  ┆ 95710526522691 ┆ Rejected       │\n",
-       "│             ┆ 7116           ┆ 504311         ┆                ┆ 74784          ┆                │\n",
-       "│ 4.6573e17   ┆ 16422462981718 ┆ 28476594917018 ┆ BasicChecking  ┆ 42921600081449 ┆ Accepted       │\n",
-       "│             ┆ 732615         ┆ 29113          ┆                ┆ 34078          ┆                │\n",
-       "│ 1.7649e19   ┆ 57638521809407 ┆ 66984868503868 ┆ UPlusFinPerson ┆ null           ┆ Rejected       │\n",
-       "│             ┆ 6406           ┆ 49814          ┆ al             ┆                ┆                │\n",
-       "│ 1.2700e19   ┆ 57638521809407 ┆ 12350877057713 ┆ BasicChecking  ┆ 42921600081449 ┆ Rejected       │\n",
-       "│             ┆ 6406           ┆ 676354         ┆                ┆ 34078          ┆                │\n",
-       "└─────────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┘"
+       "┌────────────────┬─────────────┬────────────────┬────────────────┬────────────────┬────────────────┐\n",
+       "│ PREDICTOR_0    ┆ PREDICTOR_1 ┆ PREDICTOR_3    ┆ Context_Name   ┆ IH_Web_Inbound ┆ Decision_Outco │\n",
+       "│ ---            ┆ ---         ┆ ---            ┆ ---            ┆ _Accepted_pxLa ┆ me             │\n",
+       "│ str            ┆ f64         ┆ str            ┆ str            ┆ st…            ┆ ---            │\n",
+       "│                ┆             ┆                ┆                ┆ ---            ┆ str            │\n",
+       "│                ┆             ┆                ┆                ┆ str            ┆                │\n",
+       "╞════════════════╪═════════════╪════════════════╪════════════════╪════════════════╪════════════════╡\n",
+       "│ 11607069941383 ┆ 6.0221e18   ┆ 64880866440017 ┆ FirstMortgage3 ┆ 38133941232378 ┆ Rejected       │\n",
+       "│ 855728         ┆             ┆ 20326          ┆ 0yr            ┆ 80986          ┆                │\n",
+       "│ 18278964075147 ┆ 2.2228e18   ┆ 60773377214128 ┆ FirstMortgage3 ┆ 33593793198364 ┆ Accepted       │\n",
+       "│ 118547         ┆             ┆ 85530          ┆ 0yr            ┆ 18694          ┆                │\n",
+       "│ 14791683637853 ┆ 6.8988e18   ┆ 42390258136939 ┆ MoneyMarketSav ┆ 38133941232378 ┆ Rejected       │\n",
+       "│ 151924         ┆             ┆ 51965          ┆ ingsAccount    ┆ 80986          ┆                │\n",
+       "│ 16287708547505 ┆ 3.2804e18   ┆ 60773377214128 ┆ BasicChecking  ┆ 38133941232378 ┆ Rejected       │\n",
+       "│ 018726         ┆             ┆ 85530          ┆                ┆ 80986          ┆                │\n",
+       "│ 81776116663676 ┆ 5.4996e18   ┆ 64880866440017 ┆ BasicChecking  ┆ 18378854800664 ┆ Accepted       │\n",
+       "│ 21678          ┆             ┆ 20326          ┆                ┆ 855497         ┆                │\n",
+       "│ 15369738107837 ┆ 9.6513e18   ┆ 42390258136939 ┆ UPlusFinPerson ┆ null           ┆ Rejected       │\n",
+       "│ 082215         ┆             ┆ 51965          ┆ al             ┆                ┆                │\n",
+       "│ 13418639920439 ┆ 1.3739e19   ┆ 42390258136939 ┆ BasicChecking  ┆ 18378854800664 ┆ Rejected       │\n",
+       "│ 002120         ┆             ┆ 51965          ┆                ┆ 855497         ┆                │\n",
+       "└────────────────┴─────────────┴────────────────┴────────────────┴────────────────┴────────────────┘"
       ]
      },
      "execution_count": 13,
@@ -652,10 +652,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:57.486844Z",
-     "iopub.status.busy": "2024-06-27T08:57:57.486548Z",
-     "iopub.status.idle": "2024-06-27T08:57:57.806335Z",
-     "shell.execute_reply": "2024-06-27T08:57:57.805733Z"
+     "iopub.execute_input": "2024-06-28T08:59:23.578475Z",
+     "iopub.status.busy": "2024-06-28T08:59:23.578165Z",
+     "iopub.status.idle": "2024-06-28T08:59:23.898772Z",
+     "shell.execute_reply": "2024-06-28T08:59:23.898165Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/Python/articles/explainability_extract.html b/Python/articles/explainability_extract.html
index 70e16685..043c2810 100644
--- a/Python/articles/explainability_extract.html
+++ b/Python/articles/explainability_extract.html
@@ -389,7 +389,7 @@
 <h1>Explainability Extract Analysis<a class="headerlink" href="#Explainability-Extract-Analysis" title="Link to this heading">¶</a></h1>
 <p><strong>Pega</strong></p>
 <p><strong>2024-06-11</strong></p>
-<p>Welcome to the Explainability Extract Demo. This notebook is designed to guide you through the analysis of Explainability Extract data using the DecisionData class of pdstools library. At this point, the dataset is particularly targeted on the “Arbitration” stage, but we have intentions to widen the data scope in subsequent iterations.</p>
+<p>Welcome to the Explainability Extract Demo. This notebook is designed to guide you through the analysis of Explainability Extract v1 dataset using the DecisionData class of pdstools library. At this point, the dataset is particularly targeted on the “Arbitration” stage, but we have intentions to widen the data scope in subsequent iterations. This dataset can be extracted from Infinity 24.1 and its preceding versions.</p>
 <p>We developed this notebook with a dual purpose. Firstly, we aim to familiarize you with the various functions and visualizations available in the DecisionData class. You’ll learn how to aggregate and visualize data in ways that are meaningful and insightful for your specific use cases.</p>
 <p>Secondly, we hope this notebook will inspire you. The analysis and visualizations demonstrated here are only the tip of the iceberg. We encourage you to think creatively and explore other ways this data can be utilized. Consider this notebook a springboard for your analysis journey.</p>
 <p>Each data point represents a decision made in real-time, providing a snapshot of the arbitration process. By examining this data, you have the opportunity to delve into the intricacies of these decisions, gaining a deeper understanding of the decision-making process.</p>
@@ -522,9 +522,9 @@ <h2>Action Distribution<a class="headerlink" href="#Action-Distribution" title="
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area rendered_html docutils container">
-<div>                            <div id="d70774af-4461-411e-bf4d-a8ba8437ac2a" class="plotly-graph-div" style="height:525px; width:100%;"></div>            <script type="text/javascript">                require(["plotly"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById("d70774af-4461-411e-bf4d-a8ba8437ac2a")) {                    Plotly.newPlot(                        "d70774af-4461-411e-bf4d-a8ba8437ac2a",                        [{"branchvalues":"total","domain":{"x":[0.0,1.0],"y":[0.0,1.0]},"hovertemplate":"labels=%{label}\u003cbr\u003eDecisions=%{value}\u003cbr\u003eparent=%{parent}\u003cbr\u003eid=%{id}\u003cextra\u003e\u003c\u002fextra\u003e","ids":["All Actions Arbitration\u002fAcquisition\u002fLoans\u002fpyName_101","All Actions Arbitration\u002fRetention\u002fRetailBank_1\u002fpyName_104","All Actions Arbitration\u002fAcquisition\u002fLoans\u002fpyName_109","All Actions 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@@ -937,9 +937,9 @@ <h3>Why are the actions winning<a class="headerlink" href="#Why-are-the-actions-
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diff --git a/Python/articles/explainability_extract.ipynb b/Python/articles/explainability_extract.ipynb
index b923e29e..0bcd2ff4 100644
--- a/Python/articles/explainability_extract.ipynb
+++ b/Python/articles/explainability_extract.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "__2024-06-11__\n",
     "\n",
-    "Welcome to the Explainability Extract Demo. This notebook is designed to guide you through the analysis of Explainability Extract data using the DecisionData class of pdstools library. At this point, the dataset is particularly targeted on the \"Arbitration\" stage, but we have intentions to widen the data scope in subsequent iterations.\n",
+    "Welcome to the Explainability Extract Demo. This notebook is designed to guide you through the analysis of Explainability Extract v1 dataset using the DecisionData class of pdstools library. At this point, the dataset is particularly targeted on the \"Arbitration\" stage, but we have intentions to widen the data scope in subsequent iterations. This dataset can be extracted from Infinity 24.1 and its preceding versions.\n",
     "\n",
     "We developed this notebook with a dual purpose. Firstly, we aim to familiarize you with the various functions and visualizations available in the DecisionData class. You'll learn how to aggregate and visualize data in ways that are meaningful and insightful for your specific use cases.\n",
     "\n",
@@ -26,10 +26,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:59.697108Z",
-     "iopub.status.busy": "2024-06-27T08:57:59.696944Z",
-     "iopub.status.idle": "2024-06-27T08:57:59.767172Z",
-     "shell.execute_reply": "2024-06-27T08:57:59.766620Z"
+     "iopub.execute_input": "2024-06-28T08:59:25.791507Z",
+     "iopub.status.busy": "2024-06-28T08:59:25.791343Z",
+     "iopub.status.idle": "2024-06-28T08:59:25.862826Z",
+     "shell.execute_reply": "2024-06-28T08:59:25.862309Z"
     },
     "nbsphinx": "hidden"
    },
@@ -53,10 +53,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:57:59.769447Z",
-     "iopub.status.busy": "2024-06-27T08:57:59.769036Z",
-     "iopub.status.idle": "2024-06-27T08:58:00.845926Z",
-     "shell.execute_reply": "2024-06-27T08:58:00.845295Z"
+     "iopub.execute_input": "2024-06-28T08:59:25.864963Z",
+     "iopub.status.busy": "2024-06-28T08:59:25.864793Z",
+     "iopub.status.idle": "2024-06-28T08:59:26.940307Z",
+     "shell.execute_reply": "2024-06-28T08:59:26.939785Z"
     }
    },
    "outputs": [],
@@ -77,10 +77,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:00.848504Z",
-     "iopub.status.busy": "2024-06-27T08:58:00.848074Z",
-     "iopub.status.idle": "2024-06-27T08:58:00.987756Z",
-     "shell.execute_reply": "2024-06-27T08:58:00.987242Z"
+     "iopub.execute_input": "2024-06-28T08:59:26.942798Z",
+     "iopub.status.busy": "2024-06-28T08:59:26.942379Z",
+     "iopub.status.idle": "2024-06-28T08:59:27.067041Z",
+     "shell.execute_reply": "2024-06-28T08:59:27.066459Z"
     }
    },
    "outputs": [],
@@ -111,10 +111,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:00.990003Z",
-     "iopub.status.busy": "2024-06-27T08:58:00.989722Z",
-     "iopub.status.idle": "2024-06-27T08:58:01.004229Z",
-     "shell.execute_reply": "2024-06-27T08:58:01.003702Z"
+     "iopub.execute_input": "2024-06-28T08:59:27.069764Z",
+     "iopub.status.busy": "2024-06-28T08:59:27.069312Z",
+     "iopub.status.idle": "2024-06-28T08:59:27.085363Z",
+     "shell.execute_reply": "2024-06-28T08:59:27.084909Z"
     }
    },
    "outputs": [
@@ -152,10 +152,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:01.006410Z",
-     "iopub.status.busy": "2024-06-27T08:58:01.006055Z",
-     "iopub.status.idle": "2024-06-27T08:58:01.013022Z",
-     "shell.execute_reply": "2024-06-27T08:58:01.012491Z"
+     "iopub.execute_input": "2024-06-28T08:59:27.087423Z",
+     "iopub.status.busy": "2024-06-28T08:59:27.087083Z",
+     "iopub.status.idle": "2024-06-28T08:59:27.094096Z",
+     "shell.execute_reply": "2024-06-28T08:59:27.093586Z"
     }
    },
    "outputs": [
@@ -248,10 +248,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:01.014992Z",
-     "iopub.status.busy": "2024-06-27T08:58:01.014671Z",
-     "iopub.status.idle": "2024-06-27T08:58:01.113527Z",
-     "shell.execute_reply": "2024-06-27T08:58:01.113025Z"
+     "iopub.execute_input": "2024-06-28T08:59:27.095884Z",
+     "iopub.status.busy": "2024-06-28T08:59:27.095720Z",
+     "iopub.status.idle": "2024-06-28T08:59:27.193209Z",
+     "shell.execute_reply": "2024-06-28T08:59:27.192659Z"
     }
    },
    "outputs": [
@@ -282,9 +282,9 @@
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@@ -492,19 +492,19 @@
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        "                            \n",
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@@ -557,10 +557,10 @@
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-     "shell.execute_reply": "2024-06-27T08:58:01.315430Z"
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@@ -610,19 +610,19 @@
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-     "shell.execute_reply": "2024-06-27T08:58:01.366215Z"
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        "                            \n",
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@@ -679,10 +679,10 @@
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-     "shell.execute_reply": "2024-06-27T08:58:01.417389Z"
+     "iopub.execute_input": "2024-06-28T08:59:27.448834Z",
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@@ -696,9 +696,9 @@
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                       {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('d4889bab-a1ba-42f8-a930-3615f9ed184e');\n",
+       "var gd = document.getElementById('c9c06f95-d24a-4b7c-88bc-a19cabfa2f12');\n",
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@@ -893,19 +893,19 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:01.708248Z",
-     "iopub.status.busy": "2024-06-27T08:58:01.707951Z",
-     "iopub.status.idle": "2024-06-27T08:58:01.729442Z",
-     "shell.execute_reply": "2024-06-27T08:58:01.728900Z"
+     "iopub.execute_input": "2024-06-28T08:59:27.782305Z",
+     "iopub.status.busy": "2024-06-28T08:59:27.782018Z",
+     "iopub.status.idle": "2024-06-28T08:59:27.803619Z",
+     "shell.execute_reply": "2024-06-28T08:59:27.803171Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
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diff --git a/Python/articles/pdstoolsv3.html b/Python/articles/pdstoolsv3.html
index 4113f363..b6ef9378 100644
--- a/Python/articles/pdstoolsv3.html
+++ b/Python/articles/pdstoolsv3.html
@@ -411,7 +411,7 @@ <h2>Lazy execution graphs<a class="headerlink" href="#Lazy-execution-graphs" tit
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-&lt;pdstools.adm.ADMDatamart.ADMDatamart at 0x7f961f921210&gt;
+&lt;pdstools.adm.ADMDatamart.ADMDatamart at 0x7f3554345190&gt;
 </pre></div></div>
 </div>
 <p>If you’ve used the previous version of pdstools, you’ve probably noticed nothing’s changed. You still get the same <code class="docutils literal notranslate"><span class="pre">ADMDatamart</span></code> object.</p>
@@ -450,7 +450,7 @@ <h4>NAIVE QUERY PLAN</h4><p>run <b>LazyFrame.show_graph()</b> to see the optimiz
 <g id="node2" class="node">
 <title>p2</title>
 <polygon fill="none" stroke="black" points="4857,-269 47,-269 47,-233 4857,-233 4857,-269"/>
-<text text-anchor="middle" x="2452" y="-247.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(&#45;))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical))]</text>
+<text text-anchor="middle" x="2452" y="-247.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(&#45;))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical))]</text>
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 <!-- p1&#45;&#45;p2 -->
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@@ -561,7 +561,7 @@ <h4>NAIVE QUERY PLAN</h4><p>run <b>LazyFrame.show_graph()</b> to see the optimiz
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 }
 </style>
-<small>shape: (22, 15)</small><table border="1" class="dataframe"><thead><tr><th>Name</th><th>Treatment</th><th>Positives</th><th>Direction</th><th>Configuration</th><th>Performance</th><th>SnapshotTime</th><th>Issue</th><th>ResponseCount</th><th>ModelID</th><th>Group</th><th>Channel</th><th>SuccessRate</th><th>Last_Positives</th><th>Last_ResponseCount</th></tr><tr><td>str</td><td>null</td><td>f32</td><td>cat</td><td>cat</td><td>f64</td><td>datetime[ns]</td><td>cat</td><td>f32</td><td>str</td><td>cat</td><td>cat</td><td>f32</td><td>datetime[ns]</td><td>datetime[ns]</td></tr></thead><tbody><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>1.0</td><td>&quot;Inbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.5</td><td>2021-06-01 08:23:26.789</td><td>&quot;Sales&quot;</td><td>72.0</td><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>&quot;CreditCards&quot;</td><td>&quot;Web&quot;</td><td>0.013889</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>4.0</td><td>&quot;Outbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.5</td><td>2021-06-01 08:23:26.789</td><td>&quot;Sales&quot;</td><td>166.0</td><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>&quot;CreditCards&quot;</td><td>&quot;SMS&quot;</td><td>0.024096</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>3.0</td><td>&quot;Inbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.5</td><td>2021-06-01 08:27:55.789</td><td>&quot;Sales&quot;</td><td>161.0</td><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>&quot;CreditCards&quot;</td><td>&quot;Web&quot;</td><td>0.018634</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>3.0</td><td>&quot;Inbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.5</td><td>2021-06-01 09:03:01.789</td><td>&quot;Sales&quot;</td><td>161.0</td><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>&quot;CreditCards&quot;</td><td>&quot;Web&quot;</td><td>0.018634</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>4.0</td><td>&quot;Outbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.5</td><td>2021-06-01 09:03:01.789</td><td>&quot;Sales&quot;</td><td>166.0</td><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>&quot;CreditCards&quot;</td><td>&quot;SMS&quot;</td><td>0.024096</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>21.0</td><td>&quot;Outbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.524312</td><td>2021-06-01 12:42:04.789</td><td>&quot;Sales&quot;</td><td>674.0</td><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>&quot;CreditCards&quot;</td><td>&quot;SMS&quot;</td><td>0.031157</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>24.0</td><td>&quot;Outbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.535621</td><td>2021-06-01 12:45:20.789</td><td>&quot;Sales&quot;</td><td>746.0</td><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>&quot;CreditCards&quot;</td><td>&quot;SMS&quot;</td><td>0.032172</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>43.0</td><td>&quot;Inbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.538104</td><td>2021-06-01 13:18:21.789</td><td>&quot;Sales&quot;</td><td>706.0</td><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>&quot;CreditCards&quot;</td><td>&quot;Web&quot;</td><td>0.060907</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>27.0</td><td>&quot;Outbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.531863</td><td>2021-06-01 13:23:26.789</td><td>&quot;Sales&quot;</td><td>822.0</td><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>&quot;CreditCards&quot;</td><td>&quot;SMS&quot;</td><td>0.032847</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>46.0</td><td>&quot;Inbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.519586</td><td>2021-06-01 13:23:26.789</td><td>&quot;Sales&quot;</td><td>778.0</td><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>&quot;CreditCards&quot;</td><td>&quot;Web&quot;</td><td>0.059126</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr></tbody></table></div></div>
+<small>shape: (22, 15)</small><table border="1" class="dataframe"><thead><tr><th>ModelID</th><th>Performance</th><th>Direction</th><th>Channel</th><th>ResponseCount</th><th>Configuration</th><th>Name</th><th>SnapshotTime</th><th>Treatment</th><th>Positives</th><th>Group</th><th>Issue</th><th>SuccessRate</th><th>Last_Positives</th><th>Last_ResponseCount</th></tr><tr><td>str</td><td>f64</td><td>cat</td><td>cat</td><td>f32</td><td>cat</td><td>str</td><td>datetime[ns]</td><td>null</td><td>f32</td><td>cat</td><td>cat</td><td>f32</td><td>datetime[ns]</td><td>datetime[ns]</td></tr></thead><tbody><tr><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>0.5</td><td>&quot;Inbound&quot;</td><td>&quot;Web&quot;</td><td>72.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 08:23:26.789</td><td>null</td><td>1.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.013889</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>0.5</td><td>&quot;Outbound&quot;</td><td>&quot;SMS&quot;</td><td>166.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 08:23:26.789</td><td>null</td><td>4.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.024096</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>0.5</td><td>&quot;Inbound&quot;</td><td>&quot;Web&quot;</td><td>161.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 08:27:55.789</td><td>null</td><td>3.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.018634</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>0.5</td><td>&quot;Inbound&quot;</td><td>&quot;Web&quot;</td><td>161.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 09:03:01.789</td><td>null</td><td>3.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.018634</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>0.5</td><td>&quot;Outbound&quot;</td><td>&quot;SMS&quot;</td><td>166.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 09:03:01.789</td><td>null</td><td>4.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.024096</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>0.524312</td><td>&quot;Outbound&quot;</td><td>&quot;SMS&quot;</td><td>674.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 12:42:04.789</td><td>null</td><td>21.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.031157</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>0.535621</td><td>&quot;Outbound&quot;</td><td>&quot;SMS&quot;</td><td>746.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 12:45:20.789</td><td>null</td><td>24.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.032172</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>0.538104</td><td>&quot;Inbound&quot;</td><td>&quot;Web&quot;</td><td>706.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 13:18:21.789</td><td>null</td><td>43.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.060907</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>0.531863</td><td>&quot;Outbound&quot;</td><td>&quot;SMS&quot;</td><td>822.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 13:23:26.789</td><td>null</td><td>27.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.032847</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>0.519586</td><td>&quot;Inbound&quot;</td><td>&quot;Web&quot;</td><td>778.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 13:23:26.789</td><td>null</td><td>46.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.059126</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr></tbody></table></div></div>
 </div>
 <p>This table is very similar to Pandas tables, though there are some slight differences. The types are explicitly mentioned in the header, string values are in quotes, and long string values get cut off more agressively. Other than that, there’s no real change! And if you still want to use pandas, just call <code class="docutils literal notranslate"><span class="pre">.to_pandas()</span></code> :)</p>
 <div class="nbinput docutils container">
@@ -595,18 +595,18 @@ <h4>NAIVE QUERY PLAN</h4><p>run <b>LazyFrame.show_graph()</b> to see the optimiz
   <thead>
     <tr style="text-align: right;">
       <th></th>
-      <th>Name</th>
-      <th>Treatment</th>
-      <th>Positives</th>
+      <th>ModelID</th>
+      <th>Performance</th>
       <th>Direction</th>
+      <th>Channel</th>
+      <th>ResponseCount</th>
       <th>Configuration</th>
-      <th>Performance</th>
+      <th>Name</th>
       <th>SnapshotTime</th>
-      <th>Issue</th>
-      <th>ResponseCount</th>
-      <th>ModelID</th>
+      <th>Treatment</th>
+      <th>Positives</th>
       <th>Group</th>
-      <th>Channel</th>
+      <th>Issue</th>
       <th>SuccessRate</th>
       <th>Last_Positives</th>
       <th>Last_ResponseCount</th>
@@ -615,396 +615,396 @@ <h4>NAIVE QUERY PLAN</h4><p>run <b>LazyFrame.show_graph()</b> to see the optimiz
   <tbody>
     <tr>
       <th>0</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>1.0</td>
+      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>0.500000</td>
       <td>Inbound</td>
+      <td>Web</td>
+      <td>72.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.500000</td>
+      <td>VisaGold</td>
       <td>2021-06-01 08:23:26.789</td>
-      <td>Sales</td>
-      <td>72.0</td>
-      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>None</td>
+      <td>1.0</td>
       <td>CreditCards</td>
-      <td>Web</td>
+      <td>Sales</td>
       <td>0.013889</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>4.0</td>
+      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>0.500000</td>
       <td>Outbound</td>
+      <td>SMS</td>
+      <td>166.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.500000</td>
+      <td>VisaGold</td>
       <td>2021-06-01 08:23:26.789</td>
-      <td>Sales</td>
-      <td>166.0</td>
-      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>None</td>
+      <td>4.0</td>
       <td>CreditCards</td>
-      <td>SMS</td>
+      <td>Sales</td>
       <td>0.024096</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>3.0</td>
+      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>0.500000</td>
       <td>Inbound</td>
+      <td>Web</td>
+      <td>161.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.500000</td>
+      <td>VisaGold</td>
       <td>2021-06-01 08:27:55.789</td>
-      <td>Sales</td>
-      <td>161.0</td>
-      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>None</td>
+      <td>3.0</td>
       <td>CreditCards</td>
-      <td>Web</td>
+      <td>Sales</td>
       <td>0.018634</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>3.0</td>
+      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>0.500000</td>
       <td>Inbound</td>
+      <td>Web</td>
+      <td>161.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.500000</td>
+      <td>VisaGold</td>
       <td>2021-06-01 09:03:01.789</td>
-      <td>Sales</td>
-      <td>161.0</td>
-      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>None</td>
+      <td>3.0</td>
       <td>CreditCards</td>
-      <td>Web</td>
+      <td>Sales</td>
       <td>0.018634</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>4.0</td>
+      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>0.500000</td>
       <td>Outbound</td>
+      <td>SMS</td>
+      <td>166.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.500000</td>
+      <td>VisaGold</td>
       <td>2021-06-01 09:03:01.789</td>
-      <td>Sales</td>
-      <td>166.0</td>
-      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>None</td>
+      <td>4.0</td>
       <td>CreditCards</td>
-      <td>SMS</td>
+      <td>Sales</td>
       <td>0.024096</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>5</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>7.0</td>
+      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>0.500000</td>
       <td>Outbound</td>
+      <td>SMS</td>
+      <td>271.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.500000</td>
+      <td>VisaGold</td>
       <td>2021-06-01 09:06:33.789</td>
-      <td>Sales</td>
-      <td>271.0</td>
-      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>None</td>
+      <td>7.0</td>
       <td>CreditCards</td>
-      <td>SMS</td>
+      <td>Sales</td>
       <td>0.025830</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>12.0</td>
+      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>0.554538</td>
       <td>Inbound</td>
+      <td>Web</td>
+      <td>233.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.554538</td>
+      <td>VisaGold</td>
       <td>2021-06-01 09:06:33.789</td>
-      <td>Sales</td>
-      <td>233.0</td>
-      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>None</td>
+      <td>12.0</td>
       <td>CreditCards</td>
-      <td>Web</td>
+      <td>Sales</td>
       <td>0.051502</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>7</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>12.0</td>
+      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>0.554538</td>
       <td>Inbound</td>
+      <td>Web</td>
+      <td>233.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.554538</td>
+      <td>VisaGold</td>
       <td>2021-06-01 09:34:34.789</td>
-      <td>Sales</td>
-      <td>233.0</td>
-      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>None</td>
+      <td>12.0</td>
       <td>CreditCards</td>
-      <td>Web</td>
+      <td>Sales</td>
       <td>0.051502</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>8</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>8.0</td>
+      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>0.500000</td>
       <td>Outbound</td>
+      <td>SMS</td>
+      <td>353.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.500000</td>
+      <td>VisaGold</td>
       <td>2021-06-01 09:43:16.789</td>
-      <td>Sales</td>
-      <td>353.0</td>
-      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>None</td>
+      <td>8.0</td>
       <td>CreditCards</td>
-      <td>SMS</td>
+      <td>Sales</td>
       <td>0.022663</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>9</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>19.0</td>
+      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>0.541487</td>
       <td>Inbound</td>
+      <td>Web</td>
+      <td>320.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.541487</td>
+      <td>VisaGold</td>
       <td>2021-06-01 09:43:16.789</td>
-      <td>Sales</td>
-      <td>320.0</td>
-      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>None</td>
+      <td>19.0</td>
       <td>CreditCards</td>
-      <td>Web</td>
+      <td>Sales</td>
       <td>0.059375</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>10</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>10.0</td>
+      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>0.500000</td>
       <td>Outbound</td>
+      <td>SMS</td>
+      <td>437.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.500000</td>
+      <td>VisaGold</td>
       <td>2021-06-01 10:07:35.789</td>
-      <td>Sales</td>
-      <td>437.0</td>
-      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>None</td>
+      <td>10.0</td>
       <td>CreditCards</td>
-      <td>SMS</td>
+      <td>Sales</td>
       <td>0.022883</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>11</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>24.0</td>
+      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>0.557512</td>
       <td>Inbound</td>
+      <td>Web</td>
+      <td>391.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.557512</td>
+      <td>VisaGold</td>
       <td>2021-06-01 10:07:35.789</td>
-      <td>Sales</td>
-      <td>391.0</td>
-      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>None</td>
+      <td>24.0</td>
       <td>CreditCards</td>
-      <td>Web</td>
+      <td>Sales</td>
       <td>0.061381</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>12</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>28.0</td>
+      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>0.531173</td>
       <td>Inbound</td>
+      <td>Web</td>
+      <td>470.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.531173</td>
+      <td>VisaGold</td>
       <td>2021-06-01 10:58:26.789</td>
-      <td>Sales</td>
-      <td>470.0</td>
-      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>None</td>
+      <td>28.0</td>
       <td>CreditCards</td>
-      <td>Web</td>
+      <td>Sales</td>
       <td>0.059574</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>13</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>17.0</td>
+      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>0.551713</td>
       <td>Outbound</td>
+      <td>SMS</td>
+      <td>507.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.551713</td>
+      <td>VisaGold</td>
       <td>2021-06-01 10:58:26.789</td>
-      <td>Sales</td>
-      <td>507.0</td>
-      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>None</td>
+      <td>17.0</td>
       <td>CreditCards</td>
-      <td>SMS</td>
+      <td>Sales</td>
       <td>0.033531</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>14</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>17.0</td>
+      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>0.551713</td>
       <td>Outbound</td>
+      <td>SMS</td>
+      <td>507.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.551713</td>
+      <td>VisaGold</td>
       <td>2021-06-01 11:29:54.789</td>
-      <td>Sales</td>
-      <td>507.0</td>
-      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>None</td>
+      <td>17.0</td>
       <td>CreditCards</td>
-      <td>SMS</td>
+      <td>Sales</td>
       <td>0.033531</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>15</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>21.0</td>
+      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>0.524312</td>
       <td>Outbound</td>
+      <td>SMS</td>
+      <td>674.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.524312</td>
+      <td>VisaGold</td>
       <td>2021-06-01 12:15:05.789</td>
-      <td>Sales</td>
-      <td>674.0</td>
-      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>None</td>
+      <td>21.0</td>
       <td>CreditCards</td>
-      <td>SMS</td>
+      <td>Sales</td>
       <td>0.031157</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>16</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>39.0</td>
+      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>0.549843</td>
       <td>Inbound</td>
+      <td>Web</td>
+      <td>633.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.549843</td>
+      <td>VisaGold</td>
       <td>2021-06-01 12:15:05.789</td>
-      <td>Sales</td>
-      <td>633.0</td>
-      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>None</td>
+      <td>39.0</td>
       <td>CreditCards</td>
-      <td>Web</td>
+      <td>Sales</td>
       <td>0.061611</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>17</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>21.0</td>
+      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>0.524312</td>
       <td>Outbound</td>
+      <td>SMS</td>
+      <td>674.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.524312</td>
+      <td>VisaGold</td>
       <td>2021-06-01 12:42:04.789</td>
-      <td>Sales</td>
-      <td>674.0</td>
-      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>None</td>
+      <td>21.0</td>
       <td>CreditCards</td>
-      <td>SMS</td>
+      <td>Sales</td>
       <td>0.031157</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>18</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>24.0</td>
+      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>0.535621</td>
       <td>Outbound</td>
+      <td>SMS</td>
+      <td>746.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.535621</td>
+      <td>VisaGold</td>
       <td>2021-06-01 12:45:20.789</td>
-      <td>Sales</td>
-      <td>746.0</td>
-      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>None</td>
+      <td>24.0</td>
       <td>CreditCards</td>
-      <td>SMS</td>
+      <td>Sales</td>
       <td>0.032172</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>19</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>43.0</td>
+      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>0.538104</td>
       <td>Inbound</td>
+      <td>Web</td>
+      <td>706.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.538104</td>
+      <td>VisaGold</td>
       <td>2021-06-01 13:18:21.789</td>
-      <td>Sales</td>
-      <td>706.0</td>
-      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>None</td>
+      <td>43.0</td>
       <td>CreditCards</td>
-      <td>Web</td>
+      <td>Sales</td>
       <td>0.060907</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>20</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>27.0</td>
+      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>0.531863</td>
       <td>Outbound</td>
+      <td>SMS</td>
+      <td>822.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.531863</td>
+      <td>VisaGold</td>
       <td>2021-06-01 13:23:26.789</td>
-      <td>Sales</td>
-      <td>822.0</td>
-      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>
+      <td>None</td>
+      <td>27.0</td>
       <td>CreditCards</td>
-      <td>SMS</td>
+      <td>Sales</td>
       <td>0.032847</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
     </tr>
     <tr>
       <th>21</th>
-      <td>VisaGold</td>
-      <td>None</td>
-      <td>46.0</td>
+      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>0.519586</td>
       <td>Inbound</td>
+      <td>Web</td>
+      <td>778.0</td>
       <td>OmniAdaptiveModel</td>
-      <td>0.519586</td>
+      <td>VisaGold</td>
       <td>2021-06-01 13:23:26.789</td>
-      <td>Sales</td>
-      <td>778.0</td>
-      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>
+      <td>None</td>
+      <td>46.0</td>
       <td>CreditCards</td>
-      <td>Web</td>
+      <td>Sales</td>
       <td>0.059126</td>
       <td>2021-06-01 13:23:26.789</td>
       <td>2021-06-01 13:23:26.789</td>
@@ -1105,7 +1105,7 @@ <h4>NAIVE QUERY PLAN</h4><p>run <b>LazyFrame.show_graph()</b> to see the optimiz
 <g id="node6" class="node">
 <title>p6</title>
 <polygon fill="none" stroke="black" points="4810,-430 0,-430 0,-394 4810,-394 4810,-430"/>
-<text text-anchor="middle" x="2405" y="-408.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(&#45;))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical))]</text>
+<text text-anchor="middle" x="2405" y="-408.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(&#45;))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical))]</text>
 </g>
 <!-- p5&#45;&#45;p6 -->
 <g id="edge5" class="edge">
@@ -1197,7 +1197,7 @@ <h4>NAIVE QUERY PLAN</h4><p>run <b>LazyFrame.show_graph()</b> to see the optimiz
 <g id="node14" class="node">
 <title>p14</title>
 <polygon fill="none" stroke="black" points="10714,-430 4828,-430 4828,-394 10714,-394 10714,-430"/>
-<text text-anchor="middle" x="7771" y="-408.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [col(&quot;BinPositives&quot;).strict_cast(Float32), col(&quot;GroupIndex&quot;).strict_cast(UInt32), col(&quot;PredictorName&quot;).strict_cast(Categorical(None, Physical)), col(&quot;BinType&quot;).strict_cast(Categorical(None, Physical)), col(&quot;BinIndex&quot;).strict_cast(UInt16), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;BinResponseCount&quot;).strict_cast(Float32), col(&quot;Type&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(&#45;))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;BinNegatives&quot;).strict_cast(Float32), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;EntryType&quot;).strict_cast(Categorical(None, Physical))]</text>
+<text text-anchor="middle" x="7771" y="-408.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [col(&quot;BinResponseCount&quot;).strict_cast(Float32), col(&quot;GroupIndex&quot;).strict_cast(UInt32), col(&quot;PredictorName&quot;).strict_cast(Categorical(None, Physical)), col(&quot;BinIndex&quot;).strict_cast(UInt16), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;BinNegatives&quot;).strict_cast(Float32), col(&quot;EntryType&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Type&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(&#45;))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;BinType&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;BinPositives&quot;).strict_cast(Float32)]</text>
 </g>
 <!-- p13&#45;&#45;p14 -->
 <g id="edge13" class="edge">
@@ -1276,7 +1276,7 @@ <h4>NAIVE QUERY PLAN</h4><p>run <b>LazyFrame.show_graph()</b> to see the optimiz
 <g id="node22" class="node">
 <title>p22</title>
 <polygon fill="none" stroke="black" points="15589,-286 10779,-286 10779,-250 15589,-250 15589,-286"/>
-<text text-anchor="middle" x="13184" y="-264.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(&#45;))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical))]</text>
+<text text-anchor="middle" x="13184" y="-264.3" font-family="Times,serif" font-size="14.00">WITH COLUMNS [col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(&#45;))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical))]</text>
 </g>
 <!-- p21&#45;&#45;p22 -->
 <g id="edge21" class="edge">
@@ -1365,7 +1365,7 @@ <h4>NAIVE QUERY PLAN</h4><p>run <b>LazyFrame.show_graph()</b> to see the optimiz
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-14.3 ms ± 124 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+14.3 ms ± 63.9 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
@@ -1381,7 +1381,7 @@ <h4>NAIVE QUERY PLAN</h4><p>run <b>LazyFrame.show_graph()</b> to see the optimiz
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-8.19 ms ± 41.8 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+8.51 ms ± 57.6 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)
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@@ -1489,9 +1489,9 @@ <h2>Further Polars examples<a class="headerlink" href="#Further-Polars-examples"
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@@ -1530,9 +1530,9 @@ <h2>Further Polars examples<a class="headerlink" href="#Further-Polars-examples"
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                       {"template":{"layout":{"colorway":["#001F5F","#10A5AC","#F76923","#661D34","#86CAC6","#005154","#86CAC6","#5F67B9","#FFC836","#E63690","#AC1361","#63666F","#A7A9B4","#D0D1DB"],"hovermode":"closest"}},"xaxis":{"anchor":"y","domain":[0.0,1.0],"title":{"text":"Performance"}},"yaxis":{"anchor":"x","domain":[0.0,1.0],"title":{"text":"SuccessRate"},"tickformat":"%"},"coloraxis":{"colorbar":{"title":{"text":"Performance"}},"colorscale":[[0.0,"#440154"],[0.1111111111111111,"#482878"],[0.2222222222222222,"#3e4989"],[0.3333333333333333,"#31688e"],[0.4444444444444444,"#26828e"],[0.5555555555555556,"#1f9e89"],[0.6666666666666666,"#35b779"],[0.7777777777777778,"#6ece58"],[0.8888888888888888,"#b5de2b"],[1.0,"#fde725"]]},"legend":{"tracegroupgap":0,"itemsizing":"constant"},"title":{"text":"Bubble Chart over all models \u003cbr\u003e\u003csup\u003e61 models: 7 (11.48%) at (50,0)\u003c\u002fsup\u003e\u003cbr\u003e\u003csup\u003eQuery: [(col(\"SnapshotTime\")) == (col(\"SnapshotTime\").min())]\u003c\u002fsup\u003e"}},                        {"responsive": true}                    ).then(function(){
 
-var gd = document.getElementById('f77cfd0b-40ee-4da7-b327-f04b7171abbb');
+var gd = document.getElementById('f4d8bdd4-29d5-46e5-8089-bca150582462');
 var x = new MutationObserver(function (mutations, observer) {{
         var display = window.getComputedStyle(gd).display;
         if (!display || display === 'none') {{
diff --git a/Python/articles/pdstoolsv3.ipynb b/Python/articles/pdstoolsv3.ipynb
index 407af83b..07bf2a20 100644
--- a/Python/articles/pdstoolsv3.ipynb
+++ b/Python/articles/pdstoolsv3.ipynb
@@ -23,10 +23,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:03.876963Z",
-     "iopub.status.busy": "2024-06-27T08:58:03.876800Z",
-     "iopub.status.idle": "2024-06-27T08:58:03.947870Z",
-     "shell.execute_reply": "2024-06-27T08:58:03.947324Z"
+     "iopub.execute_input": "2024-06-28T08:59:30.078044Z",
+     "iopub.status.busy": "2024-06-28T08:59:30.077877Z",
+     "iopub.status.idle": "2024-06-28T08:59:30.147985Z",
+     "shell.execute_reply": "2024-06-28T08:59:30.147459Z"
     },
     "nbsphinx": "hidden"
    },
@@ -50,17 +50,17 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:03.949847Z",
-     "iopub.status.busy": "2024-06-27T08:58:03.949668Z",
-     "iopub.status.idle": "2024-06-27T08:58:13.542857Z",
-     "shell.execute_reply": "2024-06-27T08:58:13.542212Z"
+     "iopub.execute_input": "2024-06-28T08:59:30.150170Z",
+     "iopub.status.busy": "2024-06-28T08:59:30.149759Z",
+     "iopub.status.idle": "2024-06-28T08:59:31.627062Z",
+     "shell.execute_reply": "2024-06-28T08:59:31.626433Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<pdstools.adm.ADMDatamart.ADMDatamart at 0x7f961f921210>"
+       "<pdstools.adm.ADMDatamart.ADMDatamart at 0x7f3554345190>"
       ]
      },
      "execution_count": 2,
@@ -90,10 +90,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:13.545313Z",
-     "iopub.status.busy": "2024-06-27T08:58:13.544870Z",
-     "iopub.status.idle": "2024-06-27T08:58:13.562971Z",
-     "shell.execute_reply": "2024-06-27T08:58:13.562431Z"
+     "iopub.execute_input": "2024-06-28T08:59:31.629373Z",
+     "iopub.status.busy": "2024-06-28T08:59:31.628954Z",
+     "iopub.status.idle": "2024-06-28T08:59:31.646323Z",
+     "shell.execute_reply": "2024-06-28T08:59:31.645873Z"
     }
    },
    "outputs": [
@@ -121,7 +121,7 @@
        "<g id=\"node2\" class=\"node\">\n",
        "<title>p2</title>\n",
        "<polygon fill=\"none\" stroke=\"black\" points=\"4857,-269 47,-269 47,-233 4857,-233 4857,-269\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2452\" y=\"-247.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(&#45;))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical))]</text>\n",
+       "<text text-anchor=\"middle\" x=\"2452\" y=\"-247.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(&#45;))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical))]</text>\n",
        "</g>\n",
        "<!-- p1&#45;&#45;p2 -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
@@ -167,7 +167,7 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<LazyFrame at 0x7F961F2F8850>"
+       "<LazyFrame at 0x7F35531D2350>"
       ]
      },
      "execution_count": 3,
@@ -203,60 +203,60 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:13.564950Z",
-     "iopub.status.busy": "2024-06-27T08:58:13.564652Z",
-     "iopub.status.idle": "2024-06-27T08:58:13.581607Z",
-     "shell.execute_reply": "2024-06-27T08:58:13.581076Z"
+     "iopub.execute_input": "2024-06-28T08:59:31.648228Z",
+     "iopub.status.busy": "2024-06-28T08:59:31.648052Z",
+     "iopub.status.idle": "2024-06-28T08:59:31.664761Z",
+     "shell.execute_reply": "2024-06-28T08:59:31.664310Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"5635pt\" height=\"277pt\" viewBox=\"0.00 0.00 5635.00 277.00\">\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"5614pt\" height=\"277pt\" viewBox=\"0.00 0.00 5614.00 277.00\">\n",
        "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 273)\">\n",
        "<title>polars_query</title>\n",
-       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-273 5631,-273 5631,4 -4,4\"/>\n",
+       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-273 5610,-273 5610,4 -4,4\"/>\n",
        "<!-- p1 -->\n",
        "<g id=\"node1\" class=\"node\">\n",
        "<title>p1</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"5627,-269 0,-269 0,-233 5627,-233 5627,-269\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-247.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [when([(col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;).is_not_nan()) | (col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;).is_null())]).then(col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;)).otherwise(0.0).alias(&quot;SuccessRate&quot;), when([(col(&quot;Positives&quot;).min()) == (col(&quot;Positives&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;Positives&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_Positives&quot;), when([(col(&quot;ResponseCount&quot;).min()) == (col(&quot;ResponseCount&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;ResponseCount&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_ResponseCount&quot;), [(col(&quot;Positives&quot;)) / (col(&quot;ResponseCount&quot;))].alias(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;)]</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"5606,-269 0,-269 0,-233 5606,-233 5606,-269\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-247.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [when([(col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;).is_not_nan()) | (col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;).is_null())]).then(col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;)).otherwise(0.0).alias(&quot;SuccessRate&quot;), when([(col(&quot;Positives&quot;).min()) == (col(&quot;Positives&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;Positives&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_Positives&quot;), when([(col(&quot;ResponseCount&quot;).min()) == (col(&quot;ResponseCount&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;ResponseCount&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_ResponseCount&quot;), [(col(&quot;Positives&quot;)) / (col(&quot;ResponseCount&quot;))].alias(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;)]</text>\n",
        "</g>\n",
        "<!-- p2 -->\n",
        "<g id=\"node2\" class=\"node\">\n",
        "<title>p2</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"5218.5,-197 408.5,-197 408.5,-161 5218.5,-161 5218.5,-197\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-175.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical))]</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"5208,-197 398,-197 398,-161 5208,-161 5208,-197\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-175.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical))]</text>\n",
        "</g>\n",
        "<!-- p1&#45;&#45;p2 -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
        "<title>p1--p2</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M2813.5,-232.7C2813.5,-221.85 2813.5,-207.92 2813.5,-197.1\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2803,-232.7C2803,-221.85 2803,-207.92 2803,-197.1\"/>\n",
        "</g>\n",
        "<!-- p3 -->\n",
        "<g id=\"node3\" class=\"node\">\n",
        "<title>p3</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"2855.5,-125 2771.5,-125 2771.5,-89 2855.5,-89 2855.5,-125\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-103.3\" font-family=\"Times,serif\" font-size=\"14.00\">RENAME</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"2845,-125 2761,-125 2761,-89 2845,-89 2845,-125\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-103.3\" font-family=\"Times,serif\" font-size=\"14.00\">RENAME</text>\n",
        "</g>\n",
        "<!-- p2&#45;&#45;p3 -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
        "<title>p2--p3</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M2813.5,-160.7C2813.5,-149.85 2813.5,-135.92 2813.5,-125.1\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2803,-160.7C2803,-149.85 2803,-135.92 2803,-125.1\"/>\n",
        "</g>\n",
        "<!-- p4 -->\n",
        "<g id=\"node4\" class=\"node\">\n",
        "<title>p4</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"2851,-53 2776,-53 2776,0 2851,0 2851,-53\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">TABLE</text>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">π 12/27;</text>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">σ None</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"2840.5,-53 2765.5,-53 2765.5,0 2840.5,0 2840.5,-53\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">TABLE</text>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">π 12/27;</text>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">σ None</text>\n",
        "</g>\n",
        "<!-- p3&#45;&#45;p4 -->\n",
        "<g id=\"edge3\" class=\"edge\">\n",
        "<title>p3--p4</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M2813.5,-88.97C2813.5,-78.51 2813.5,-64.9 2813.5,-53.16\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2803,-88.97C2803,-78.51 2803,-64.9 2803,-53.16\"/>\n",
        "</g>\n",
        "</g>\n",
        "</svg>"
@@ -290,71 +290,71 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:13.583529Z",
-     "iopub.status.busy": "2024-06-27T08:58:13.583238Z",
-     "iopub.status.idle": "2024-06-27T08:58:14.930555Z",
-     "shell.execute_reply": "2024-06-27T08:58:14.929982Z"
+     "iopub.execute_input": "2024-06-28T08:59:31.666688Z",
+     "iopub.status.busy": "2024-06-28T08:59:31.666391Z",
+     "iopub.status.idle": "2024-06-28T08:59:32.050298Z",
+     "shell.execute_reply": "2024-06-28T08:59:32.049698Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"5635pt\" height=\"349pt\" viewBox=\"0.00 0.00 5635.00 349.00\">\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"5614pt\" height=\"349pt\" viewBox=\"0.00 0.00 5614.00 349.00\">\n",
        "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 345)\">\n",
        "<title>polars_query</title>\n",
-       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-345 5631,-345 5631,4 -4,4\"/>\n",
+       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-345 5610,-345 5610,4 -4,4\"/>\n",
        "<!-- p1 -->\n",
        "<g id=\"node1\" class=\"node\">\n",
        "<title>p1</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"3002.5,-341 2624.5,-341 2624.5,-305 3002.5,-305 3002.5,-341\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-319.3\" font-family=\"Times,serif\" font-size=\"14.00\">FILTER BY col(&quot;Name&quot;).str.contains([String(Visa)])</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"2992,-341 2614,-341 2614,-305 2992,-305 2992,-341\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-319.3\" font-family=\"Times,serif\" font-size=\"14.00\">FILTER BY col(&quot;Name&quot;).str.contains([String(Visa)])</text>\n",
        "</g>\n",
        "<!-- p2 -->\n",
        "<g id=\"node2\" class=\"node\">\n",
        "<title>p2</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"5627,-269 0,-269 0,-233 5627,-233 5627,-269\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-247.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [when([(col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;).is_not_nan()) | (col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;).is_null())]).then(col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;)).otherwise(0.0).alias(&quot;SuccessRate&quot;), when([(col(&quot;Positives&quot;).min()) == (col(&quot;Positives&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;Positives&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_Positives&quot;), when([(col(&quot;ResponseCount&quot;).min()) == (col(&quot;ResponseCount&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;ResponseCount&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_ResponseCount&quot;), [(col(&quot;Positives&quot;)) / (col(&quot;ResponseCount&quot;))].alias(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;)]</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"5606,-269 0,-269 0,-233 5606,-233 5606,-269\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-247.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [when([(col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;).is_not_nan()) | (col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;).is_null())]).then(col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;)).otherwise(0.0).alias(&quot;SuccessRate&quot;), when([(col(&quot;Positives&quot;).min()) == (col(&quot;Positives&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;Positives&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_Positives&quot;), when([(col(&quot;ResponseCount&quot;).min()) == (col(&quot;ResponseCount&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;ResponseCount&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_ResponseCount&quot;), [(col(&quot;Positives&quot;)) / (col(&quot;ResponseCount&quot;))].alias(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;)]</text>\n",
        "</g>\n",
        "<!-- p1&#45;&#45;p2 -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
        "<title>p1--p2</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M2813.5,-304.7C2813.5,-293.85 2813.5,-279.92 2813.5,-269.1\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2803,-304.7C2803,-293.85 2803,-279.92 2803,-269.1\"/>\n",
        "</g>\n",
        "<!-- p3 -->\n",
        "<g id=\"node3\" class=\"node\">\n",
        "<title>p3</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"5218.5,-197 408.5,-197 408.5,-161 5218.5,-161 5218.5,-197\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-175.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical))]</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"5208,-197 398,-197 398,-161 5208,-161 5208,-197\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-175.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical))]</text>\n",
        "</g>\n",
        "<!-- p2&#45;&#45;p3 -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
        "<title>p2--p3</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M2813.5,-232.7C2813.5,-221.85 2813.5,-207.92 2813.5,-197.1\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2803,-232.7C2803,-221.85 2803,-207.92 2803,-197.1\"/>\n",
        "</g>\n",
        "<!-- p4 -->\n",
        "<g id=\"node4\" class=\"node\">\n",
        "<title>p4</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"2855.5,-125 2771.5,-125 2771.5,-89 2855.5,-89 2855.5,-125\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-103.3\" font-family=\"Times,serif\" font-size=\"14.00\">RENAME</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"2845,-125 2761,-125 2761,-89 2845,-89 2845,-125\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-103.3\" font-family=\"Times,serif\" font-size=\"14.00\">RENAME</text>\n",
        "</g>\n",
        "<!-- p3&#45;&#45;p4 -->\n",
        "<g id=\"edge3\" class=\"edge\">\n",
        "<title>p3--p4</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M2813.5,-160.7C2813.5,-149.85 2813.5,-135.92 2813.5,-125.1\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2803,-160.7C2803,-149.85 2803,-135.92 2803,-125.1\"/>\n",
        "</g>\n",
        "<!-- p5 -->\n",
        "<g id=\"node5\" class=\"node\">\n",
        "<title>p5</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"2969,-53 2658,-53 2658,0 2969,0 2969,-53\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">TABLE</text>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">π 12/27;</text>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">σ col(&quot;Name&quot;).str.contains([String(Visa)])</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"2958.5,-53 2647.5,-53 2647.5,0 2958.5,0 2958.5,-53\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">TABLE</text>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">π 12/27;</text>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">σ col(&quot;Name&quot;).str.contains([String(Visa)])</text>\n",
        "</g>\n",
        "<!-- p4&#45;&#45;p5 -->\n",
        "<g id=\"edge4\" class=\"edge\">\n",
        "<title>p4--p5</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M2813.5,-88.97C2813.5,-78.51 2813.5,-64.9 2813.5,-53.16\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2803,-88.97C2803,-78.51 2803,-64.9 2803,-53.16\"/>\n",
        "</g>\n",
        "</g>\n",
        "</svg>"
@@ -387,10 +387,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:14.932861Z",
-     "iopub.status.busy": "2024-06-27T08:58:14.932454Z",
-     "iopub.status.idle": "2024-06-27T08:58:14.939724Z",
-     "shell.execute_reply": "2024-06-27T08:58:14.939157Z"
+     "iopub.execute_input": "2024-06-28T08:59:32.052579Z",
+     "iopub.status.busy": "2024-06-28T08:59:32.052238Z",
+     "iopub.status.idle": "2024-06-28T08:59:32.059430Z",
+     "shell.execute_reply": "2024-06-28T08:59:32.058999Z"
     }
    },
    "outputs": [
@@ -404,49 +404,59 @@
        "  white-space: pre-wrap;\n",
        "}\n",
        "</style>\n",
-       "<small>shape: (22, 15)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Name</th><th>Treatment</th><th>Positives</th><th>Direction</th><th>Configuration</th><th>Performance</th><th>SnapshotTime</th><th>Issue</th><th>ResponseCount</th><th>ModelID</th><th>Group</th><th>Channel</th><th>SuccessRate</th><th>Last_Positives</th><th>Last_ResponseCount</th></tr><tr><td>str</td><td>null</td><td>f32</td><td>cat</td><td>cat</td><td>f64</td><td>datetime[ns]</td><td>cat</td><td>f32</td><td>str</td><td>cat</td><td>cat</td><td>f32</td><td>datetime[ns]</td><td>datetime[ns]</td></tr></thead><tbody><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>1.0</td><td>&quot;Inbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.5</td><td>2021-06-01 08:23:26.789</td><td>&quot;Sales&quot;</td><td>72.0</td><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>&quot;CreditCards&quot;</td><td>&quot;Web&quot;</td><td>0.013889</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>4.0</td><td>&quot;Outbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.5</td><td>2021-06-01 08:23:26.789</td><td>&quot;Sales&quot;</td><td>166.0</td><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>&quot;CreditCards&quot;</td><td>&quot;SMS&quot;</td><td>0.024096</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>3.0</td><td>&quot;Inbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.5</td><td>2021-06-01 08:27:55.789</td><td>&quot;Sales&quot;</td><td>161.0</td><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>&quot;CreditCards&quot;</td><td>&quot;Web&quot;</td><td>0.018634</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>3.0</td><td>&quot;Inbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.5</td><td>2021-06-01 09:03:01.789</td><td>&quot;Sales&quot;</td><td>161.0</td><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>&quot;CreditCards&quot;</td><td>&quot;Web&quot;</td><td>0.018634</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>4.0</td><td>&quot;Outbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.5</td><td>2021-06-01 09:03:01.789</td><td>&quot;Sales&quot;</td><td>166.0</td><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>&quot;CreditCards&quot;</td><td>&quot;SMS&quot;</td><td>0.024096</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>21.0</td><td>&quot;Outbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.524312</td><td>2021-06-01 12:42:04.789</td><td>&quot;Sales&quot;</td><td>674.0</td><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>&quot;CreditCards&quot;</td><td>&quot;SMS&quot;</td><td>0.031157</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>24.0</td><td>&quot;Outbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.535621</td><td>2021-06-01 12:45:20.789</td><td>&quot;Sales&quot;</td><td>746.0</td><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>&quot;CreditCards&quot;</td><td>&quot;SMS&quot;</td><td>0.032172</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>43.0</td><td>&quot;Inbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.538104</td><td>2021-06-01 13:18:21.789</td><td>&quot;Sales&quot;</td><td>706.0</td><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>&quot;CreditCards&quot;</td><td>&quot;Web&quot;</td><td>0.060907</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>27.0</td><td>&quot;Outbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.531863</td><td>2021-06-01 13:23:26.789</td><td>&quot;Sales&quot;</td><td>822.0</td><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>&quot;CreditCards&quot;</td><td>&quot;SMS&quot;</td><td>0.032847</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;VisaGold&quot;</td><td>null</td><td>46.0</td><td>&quot;Inbound&quot;</td><td>&quot;OmniAdaptiveModel&quot;</td><td>0.519586</td><td>2021-06-01 13:23:26.789</td><td>&quot;Sales&quot;</td><td>778.0</td><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>&quot;CreditCards&quot;</td><td>&quot;Web&quot;</td><td>0.059126</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr></tbody></table></div>"
+       "<small>shape: (22, 15)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>ModelID</th><th>Performance</th><th>Direction</th><th>Channel</th><th>ResponseCount</th><th>Configuration</th><th>Name</th><th>SnapshotTime</th><th>Treatment</th><th>Positives</th><th>Group</th><th>Issue</th><th>SuccessRate</th><th>Last_Positives</th><th>Last_ResponseCount</th></tr><tr><td>str</td><td>f64</td><td>cat</td><td>cat</td><td>f32</td><td>cat</td><td>str</td><td>datetime[ns]</td><td>null</td><td>f32</td><td>cat</td><td>cat</td><td>f32</td><td>datetime[ns]</td><td>datetime[ns]</td></tr></thead><tbody><tr><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>0.5</td><td>&quot;Inbound&quot;</td><td>&quot;Web&quot;</td><td>72.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 08:23:26.789</td><td>null</td><td>1.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.013889</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>0.5</td><td>&quot;Outbound&quot;</td><td>&quot;SMS&quot;</td><td>166.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 08:23:26.789</td><td>null</td><td>4.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.024096</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>0.5</td><td>&quot;Inbound&quot;</td><td>&quot;Web&quot;</td><td>161.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 08:27:55.789</td><td>null</td><td>3.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.018634</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>0.5</td><td>&quot;Inbound&quot;</td><td>&quot;Web&quot;</td><td>161.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 09:03:01.789</td><td>null</td><td>3.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.018634</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>0.5</td><td>&quot;Outbound&quot;</td><td>&quot;SMS&quot;</td><td>166.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 09:03:01.789</td><td>null</td><td>4.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.024096</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>0.524312</td><td>&quot;Outbound&quot;</td><td>&quot;SMS&quot;</td><td>674.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 12:42:04.789</td><td>null</td><td>21.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.031157</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>0.535621</td><td>&quot;Outbound&quot;</td><td>&quot;SMS&quot;</td><td>746.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 12:45:20.789</td><td>null</td><td>24.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.032172</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>0.538104</td><td>&quot;Inbound&quot;</td><td>&quot;Web&quot;</td><td>706.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 13:18:21.789</td><td>null</td><td>43.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.060907</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;1be7cd72-741b-5d68-b148-c1af01…</td><td>0.531863</td><td>&quot;Outbound&quot;</td><td>&quot;SMS&quot;</td><td>822.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 13:23:26.789</td><td>null</td><td>27.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.032847</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr><tr><td>&quot;17571d69-f802-5122-9bfe-6f3f4e…</td><td>0.519586</td><td>&quot;Inbound&quot;</td><td>&quot;Web&quot;</td><td>778.0</td><td>&quot;OmniAdaptiveModel&quot;</td><td>&quot;VisaGold&quot;</td><td>2021-06-01 13:23:26.789</td><td>null</td><td>46.0</td><td>&quot;CreditCards&quot;</td><td>&quot;Sales&quot;</td><td>0.059126</td><td>2021-06-01 13:23:26.789</td><td>2021-06-01 13:23:26.789</td></tr></tbody></table></div>"
       ],
       "text/plain": [
        "shape: (22, 15)\n",
-       "┌──────────┬───────────┬───────────┬───────────┬───┬─────────┬────────────┬────────────┬───────────┐\n",
-       "│ Name     ┆ Treatment ┆ Positives ┆ Direction ┆ … ┆ Channel ┆ SuccessRat ┆ Last_Posit ┆ Last_Resp │\n",
-       "│ ---      ┆ ---       ┆ ---       ┆ ---       ┆   ┆ ---     ┆ e          ┆ ives       ┆ onseCount │\n",
-       "│ str      ┆ null      ┆ f32       ┆ cat       ┆   ┆ cat     ┆ ---        ┆ ---        ┆ ---       │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆ f32        ┆ datetime[n ┆ datetime[ │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ s]         ┆ ns]       │\n",
-       "╞══════════╪═══════════╪═══════════╪═══════════╪═══╪═════════╪════════════╪════════════╪═══════════╡\n",
-       "│ VisaGold ┆ null      ┆ 1.0       ┆ Inbound   ┆ … ┆ Web     ┆ 0.013889   ┆ 2021-06-01 ┆ 2021-06-0 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 13:23:26.7 ┆ 1 13:23:2 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 89         ┆ 6.789     │\n",
-       "│ VisaGold ┆ null      ┆ 4.0       ┆ Outbound  ┆ … ┆ SMS     ┆ 0.024096   ┆ 2021-06-01 ┆ 2021-06-0 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 13:23:26.7 ┆ 1 13:23:2 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 89         ┆ 6.789     │\n",
-       "│ VisaGold ┆ null      ┆ 3.0       ┆ Inbound   ┆ … ┆ Web     ┆ 0.018634   ┆ 2021-06-01 ┆ 2021-06-0 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 13:23:26.7 ┆ 1 13:23:2 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 89         ┆ 6.789     │\n",
-       "│ VisaGold ┆ null      ┆ 3.0       ┆ Inbound   ┆ … ┆ Web     ┆ 0.018634   ┆ 2021-06-01 ┆ 2021-06-0 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 13:23:26.7 ┆ 1 13:23:2 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 89         ┆ 6.789     │\n",
-       "│ VisaGold ┆ null      ┆ 4.0       ┆ Outbound  ┆ … ┆ SMS     ┆ 0.024096   ┆ 2021-06-01 ┆ 2021-06-0 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 13:23:26.7 ┆ 1 13:23:2 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 89         ┆ 6.789     │\n",
-       "│ …        ┆ …         ┆ …         ┆ …         ┆ … ┆ …       ┆ …          ┆ …          ┆ …         │\n",
-       "│ VisaGold ┆ null      ┆ 21.0      ┆ Outbound  ┆ … ┆ SMS     ┆ 0.031157   ┆ 2021-06-01 ┆ 2021-06-0 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 13:23:26.7 ┆ 1 13:23:2 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 89         ┆ 6.789     │\n",
-       "│ VisaGold ┆ null      ┆ 24.0      ┆ Outbound  ┆ … ┆ SMS     ┆ 0.032172   ┆ 2021-06-01 ┆ 2021-06-0 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 13:23:26.7 ┆ 1 13:23:2 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 89         ┆ 6.789     │\n",
-       "│ VisaGold ┆ null      ┆ 43.0      ┆ Inbound   ┆ … ┆ Web     ┆ 0.060907   ┆ 2021-06-01 ┆ 2021-06-0 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 13:23:26.7 ┆ 1 13:23:2 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 89         ┆ 6.789     │\n",
-       "│ VisaGold ┆ null      ┆ 27.0      ┆ Outbound  ┆ … ┆ SMS     ┆ 0.032847   ┆ 2021-06-01 ┆ 2021-06-0 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 13:23:26.7 ┆ 1 13:23:2 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 89         ┆ 6.789     │\n",
-       "│ VisaGold ┆ null      ┆ 46.0      ┆ Inbound   ┆ … ┆ Web     ┆ 0.059126   ┆ 2021-06-01 ┆ 2021-06-0 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 13:23:26.7 ┆ 1 13:23:2 │\n",
-       "│          ┆           ┆           ┆           ┆   ┆         ┆            ┆ 89         ┆ 6.789     │\n",
-       "└──────────┴───────────┴───────────┴───────────┴───┴─────────┴────────────┴────────────┴───────────┘"
+       "┌────────────┬────────────┬───────────┬─────────┬───┬───────┬────────────┬────────────┬────────────┐\n",
+       "│ ModelID    ┆ Performanc ┆ Direction ┆ Channel ┆ … ┆ Issue ┆ SuccessRat ┆ Last_Posit ┆ Last_Respo │\n",
+       "│ ---        ┆ e          ┆ ---       ┆ ---     ┆   ┆ ---   ┆ e          ┆ ives       ┆ nseCount   │\n",
+       "│ str        ┆ ---        ┆ cat       ┆ cat     ┆   ┆ cat   ┆ ---        ┆ ---        ┆ ---        │\n",
+       "│            ┆ f64        ┆           ┆         ┆   ┆       ┆ f32        ┆ datetime[n ┆ datetime[n │\n",
+       "│            ┆            ┆           ┆         ┆   ┆       ┆            ┆ s]         ┆ s]         │\n",
+       "╞════════════╪════════════╪═══════════╪═════════╪═══╪═══════╪════════════╪════════════╪════════════╡\n",
+       "│ 17571d69-f ┆ 0.5        ┆ Inbound   ┆ Web     ┆ … ┆ Sales ┆ 0.013889   ┆ 2021-06-01 ┆ 2021-06-01 │\n",
+       "│ 802-5122-9 ┆            ┆           ┆         ┆   ┆       ┆            ┆ 13:23:26.7 ┆ 13:23:26.7 │\n",
+       "│ bfe-6f3f4e ┆            ┆           ┆         ┆   ┆       ┆            ┆ 89         ┆ 89         │\n",
+       "│ …          ┆            ┆           ┆         ┆   ┆       ┆            ┆            ┆            │\n",
+       "│ 1be7cd72-7 ┆ 0.5        ┆ Outbound  ┆ SMS     ┆ … ┆ Sales ┆ 0.024096   ┆ 2021-06-01 ┆ 2021-06-01 │\n",
+       "│ 41b-5d68-b ┆            ┆           ┆         ┆   ┆       ┆            ┆ 13:23:26.7 ┆ 13:23:26.7 │\n",
+       "│ 148-c1af01 ┆            ┆           ┆         ┆   ┆       ┆            ┆ 89         ┆ 89         │\n",
+       "│ …          ┆            ┆           ┆         ┆   ┆       ┆            ┆            ┆            │\n",
+       "│ 17571d69-f ┆ 0.5        ┆ Inbound   ┆ Web     ┆ … ┆ Sales ┆ 0.018634   ┆ 2021-06-01 ┆ 2021-06-01 │\n",
+       "│ 802-5122-9 ┆            ┆           ┆         ┆   ┆       ┆            ┆ 13:23:26.7 ┆ 13:23:26.7 │\n",
+       "│ bfe-6f3f4e ┆            ┆           ┆         ┆   ┆       ┆            ┆ 89         ┆ 89         │\n",
+       "│ …          ┆            ┆           ┆         ┆   ┆       ┆            ┆            ┆            │\n",
+       "│ 17571d69-f ┆ 0.5        ┆ Inbound   ┆ Web     ┆ … ┆ Sales ┆ 0.018634   ┆ 2021-06-01 ┆ 2021-06-01 │\n",
+       "│ 802-5122-9 ┆            ┆           ┆         ┆   ┆       ┆            ┆ 13:23:26.7 ┆ 13:23:26.7 │\n",
+       "│ bfe-6f3f4e ┆            ┆           ┆         ┆   ┆       ┆            ┆ 89         ┆ 89         │\n",
+       "│ …          ┆            ┆           ┆         ┆   ┆       ┆            ┆            ┆            │\n",
+       "│ 1be7cd72-7 ┆ 0.5        ┆ Outbound  ┆ SMS     ┆ … ┆ Sales ┆ 0.024096   ┆ 2021-06-01 ┆ 2021-06-01 │\n",
+       "│ 41b-5d68-b ┆            ┆           ┆         ┆   ┆       ┆            ┆ 13:23:26.7 ┆ 13:23:26.7 │\n",
+       "│ 148-c1af01 ┆            ┆           ┆         ┆   ┆       ┆            ┆ 89         ┆ 89         │\n",
+       "│ …          ┆            ┆           ┆         ┆   ┆       ┆            ┆            ┆            │\n",
+       "│ …          ┆ …          ┆ …         ┆ …       ┆ … ┆ …     ┆ …          ┆ …          ┆ …          │\n",
+       "│ 1be7cd72-7 ┆ 0.524312   ┆ Outbound  ┆ SMS     ┆ … ┆ Sales ┆ 0.031157   ┆ 2021-06-01 ┆ 2021-06-01 │\n",
+       "│ 41b-5d68-b ┆            ┆           ┆         ┆   ┆       ┆            ┆ 13:23:26.7 ┆ 13:23:26.7 │\n",
+       "│ 148-c1af01 ┆            ┆           ┆         ┆   ┆       ┆            ┆ 89         ┆ 89         │\n",
+       "│ …          ┆            ┆           ┆         ┆   ┆       ┆            ┆            ┆            │\n",
+       "│ 1be7cd72-7 ┆ 0.535621   ┆ Outbound  ┆ SMS     ┆ … ┆ Sales ┆ 0.032172   ┆ 2021-06-01 ┆ 2021-06-01 │\n",
+       "│ 41b-5d68-b ┆            ┆           ┆         ┆   ┆       ┆            ┆ 13:23:26.7 ┆ 13:23:26.7 │\n",
+       "│ 148-c1af01 ┆            ┆           ┆         ┆   ┆       ┆            ┆ 89         ┆ 89         │\n",
+       "│ …          ┆            ┆           ┆         ┆   ┆       ┆            ┆            ┆            │\n",
+       "│ 17571d69-f ┆ 0.538104   ┆ Inbound   ┆ Web     ┆ … ┆ Sales ┆ 0.060907   ┆ 2021-06-01 ┆ 2021-06-01 │\n",
+       "│ 802-5122-9 ┆            ┆           ┆         ┆   ┆       ┆            ┆ 13:23:26.7 ┆ 13:23:26.7 │\n",
+       "│ bfe-6f3f4e ┆            ┆           ┆         ┆   ┆       ┆            ┆ 89         ┆ 89         │\n",
+       "│ …          ┆            ┆           ┆         ┆   ┆       ┆            ┆            ┆            │\n",
+       "│ 1be7cd72-7 ┆ 0.531863   ┆ Outbound  ┆ SMS     ┆ … ┆ Sales ┆ 0.032847   ┆ 2021-06-01 ┆ 2021-06-01 │\n",
+       "│ 41b-5d68-b ┆            ┆           ┆         ┆   ┆       ┆            ┆ 13:23:26.7 ┆ 13:23:26.7 │\n",
+       "│ 148-c1af01 ┆            ┆           ┆         ┆   ┆       ┆            ┆ 89         ┆ 89         │\n",
+       "│ …          ┆            ┆           ┆         ┆   ┆       ┆            ┆            ┆            │\n",
+       "│ 17571d69-f ┆ 0.519586   ┆ Inbound   ┆ Web     ┆ … ┆ Sales ┆ 0.059126   ┆ 2021-06-01 ┆ 2021-06-01 │\n",
+       "│ 802-5122-9 ┆            ┆           ┆         ┆   ┆       ┆            ┆ 13:23:26.7 ┆ 13:23:26.7 │\n",
+       "│ bfe-6f3f4e ┆            ┆           ┆         ┆   ┆       ┆            ┆ 89         ┆ 89         │\n",
+       "│ …          ┆            ┆           ┆         ┆   ┆       ┆            ┆            ┆            │\n",
+       "└────────────┴────────────┴───────────┴─────────┴───┴───────┴────────────┴────────────┴────────────┘"
       ]
      },
      "execution_count": 6,
@@ -471,10 +481,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:14.941738Z",
-     "iopub.status.busy": "2024-06-27T08:58:14.941358Z",
-     "iopub.status.idle": "2024-06-27T08:58:14.962707Z",
-     "shell.execute_reply": "2024-06-27T08:58:14.962117Z"
+     "iopub.execute_input": "2024-06-28T08:59:32.061406Z",
+     "iopub.status.busy": "2024-06-28T08:59:32.061094Z",
+     "iopub.status.idle": "2024-06-28T08:59:32.081600Z",
+     "shell.execute_reply": "2024-06-28T08:59:32.081154Z"
     }
    },
    "outputs": [
@@ -499,18 +509,18 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Treatment</th>\n",
-       "      <th>Positives</th>\n",
+       "      <th>ModelID</th>\n",
+       "      <th>Performance</th>\n",
        "      <th>Direction</th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>ResponseCount</th>\n",
        "      <th>Configuration</th>\n",
-       "      <th>Performance</th>\n",
+       "      <th>Name</th>\n",
        "      <th>SnapshotTime</th>\n",
-       "      <th>Issue</th>\n",
-       "      <th>ResponseCount</th>\n",
-       "      <th>ModelID</th>\n",
+       "      <th>Treatment</th>\n",
+       "      <th>Positives</th>\n",
        "      <th>Group</th>\n",
-       "      <th>Channel</th>\n",
+       "      <th>Issue</th>\n",
        "      <th>SuccessRate</th>\n",
        "      <th>Last_Positives</th>\n",
        "      <th>Last_ResponseCount</th>\n",
@@ -519,396 +529,396 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>0.500000</td>\n",
        "      <td>Inbound</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>72.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.500000</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 08:23:26.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>Web</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.013889</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>4.0</td>\n",
+       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>0.500000</td>\n",
        "      <td>Outbound</td>\n",
+       "      <td>SMS</td>\n",
+       "      <td>166.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.500000</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 08:23:26.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>166.0</td>\n",
-       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>SMS</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.024096</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>0.500000</td>\n",
        "      <td>Inbound</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>161.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.500000</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 08:27:55.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>161.0</td>\n",
-       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>None</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>Web</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.018634</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>0.500000</td>\n",
        "      <td>Inbound</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>161.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.500000</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 09:03:01.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>161.0</td>\n",
-       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>None</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>Web</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.018634</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>4.0</td>\n",
+       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>0.500000</td>\n",
        "      <td>Outbound</td>\n",
+       "      <td>SMS</td>\n",
+       "      <td>166.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.500000</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 09:03:01.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>166.0</td>\n",
-       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>SMS</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.024096</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>7.0</td>\n",
+       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>0.500000</td>\n",
        "      <td>Outbound</td>\n",
+       "      <td>SMS</td>\n",
+       "      <td>271.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.500000</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 09:06:33.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>271.0</td>\n",
-       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>None</td>\n",
+       "      <td>7.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>SMS</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.025830</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>12.0</td>\n",
+       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>0.554538</td>\n",
        "      <td>Inbound</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>233.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.554538</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 09:06:33.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>233.0</td>\n",
-       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>None</td>\n",
+       "      <td>12.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>Web</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.051502</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>12.0</td>\n",
+       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>0.554538</td>\n",
        "      <td>Inbound</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>233.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.554538</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 09:34:34.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>233.0</td>\n",
-       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>None</td>\n",
+       "      <td>12.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>Web</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.051502</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>8.0</td>\n",
+       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>0.500000</td>\n",
        "      <td>Outbound</td>\n",
+       "      <td>SMS</td>\n",
+       "      <td>353.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.500000</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 09:43:16.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>353.0</td>\n",
-       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>None</td>\n",
+       "      <td>8.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>SMS</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.022663</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>19.0</td>\n",
+       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>0.541487</td>\n",
        "      <td>Inbound</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>320.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.541487</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 09:43:16.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>320.0</td>\n",
-       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>None</td>\n",
+       "      <td>19.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>Web</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.059375</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>10.0</td>\n",
+       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>0.500000</td>\n",
        "      <td>Outbound</td>\n",
+       "      <td>SMS</td>\n",
+       "      <td>437.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.500000</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 10:07:35.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>437.0</td>\n",
-       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>None</td>\n",
+       "      <td>10.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>SMS</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.022883</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>24.0</td>\n",
+       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>0.557512</td>\n",
        "      <td>Inbound</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>391.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.557512</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 10:07:35.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>391.0</td>\n",
-       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>None</td>\n",
+       "      <td>24.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>Web</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.061381</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>28.0</td>\n",
+       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>0.531173</td>\n",
        "      <td>Inbound</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>470.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.531173</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 10:58:26.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>470.0</td>\n",
-       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>None</td>\n",
+       "      <td>28.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>Web</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.059574</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>17.0</td>\n",
+       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>0.551713</td>\n",
        "      <td>Outbound</td>\n",
+       "      <td>SMS</td>\n",
+       "      <td>507.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.551713</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 10:58:26.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>507.0</td>\n",
-       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>None</td>\n",
+       "      <td>17.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>SMS</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.033531</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>17.0</td>\n",
+       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>0.551713</td>\n",
        "      <td>Outbound</td>\n",
+       "      <td>SMS</td>\n",
+       "      <td>507.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.551713</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 11:29:54.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>507.0</td>\n",
-       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>None</td>\n",
+       "      <td>17.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>SMS</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.033531</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>21.0</td>\n",
+       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>0.524312</td>\n",
        "      <td>Outbound</td>\n",
+       "      <td>SMS</td>\n",
+       "      <td>674.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.524312</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 12:15:05.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>674.0</td>\n",
-       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>None</td>\n",
+       "      <td>21.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>SMS</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.031157</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>39.0</td>\n",
+       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>0.549843</td>\n",
        "      <td>Inbound</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>633.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.549843</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 12:15:05.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>633.0</td>\n",
-       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>None</td>\n",
+       "      <td>39.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>Web</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.061611</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>21.0</td>\n",
+       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>0.524312</td>\n",
        "      <td>Outbound</td>\n",
+       "      <td>SMS</td>\n",
+       "      <td>674.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.524312</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 12:42:04.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>674.0</td>\n",
-       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>None</td>\n",
+       "      <td>21.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>SMS</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.031157</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>24.0</td>\n",
+       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>0.535621</td>\n",
        "      <td>Outbound</td>\n",
+       "      <td>SMS</td>\n",
+       "      <td>746.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.535621</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 12:45:20.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>746.0</td>\n",
-       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>None</td>\n",
+       "      <td>24.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>SMS</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.032172</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>43.0</td>\n",
+       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>0.538104</td>\n",
        "      <td>Inbound</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>706.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.538104</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 13:18:21.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>706.0</td>\n",
-       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>None</td>\n",
+       "      <td>43.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>Web</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.060907</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>27.0</td>\n",
+       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>0.531863</td>\n",
        "      <td>Outbound</td>\n",
+       "      <td>SMS</td>\n",
+       "      <td>822.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.531863</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>822.0</td>\n",
-       "      <td>1be7cd72-741b-5d68-b148-c1af0142fd4a</td>\n",
+       "      <td>None</td>\n",
+       "      <td>27.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>SMS</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.032847</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
-       "      <td>VisaGold</td>\n",
-       "      <td>None</td>\n",
-       "      <td>46.0</td>\n",
+       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>0.519586</td>\n",
        "      <td>Inbound</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>778.0</td>\n",
        "      <td>OmniAdaptiveModel</td>\n",
-       "      <td>0.519586</td>\n",
+       "      <td>VisaGold</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>778.0</td>\n",
-       "      <td>17571d69-f802-5122-9bfe-6f3f4ef9073b</td>\n",
+       "      <td>None</td>\n",
+       "      <td>46.0</td>\n",
        "      <td>CreditCards</td>\n",
-       "      <td>Web</td>\n",
+       "      <td>Sales</td>\n",
        "      <td>0.059126</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
        "      <td>2021-06-01 13:23:26.789</td>\n",
@@ -918,77 +928,77 @@
        "</div>"
       ],
       "text/plain": [
-       "        Name Treatment  Positives Direction      Configuration  Performance  \\\n",
-       "0   VisaGold      None        1.0   Inbound  OmniAdaptiveModel     0.500000   \n",
-       "1   VisaGold      None        4.0  Outbound  OmniAdaptiveModel     0.500000   \n",
-       "2   VisaGold      None        3.0   Inbound  OmniAdaptiveModel     0.500000   \n",
-       "3   VisaGold      None        3.0   Inbound  OmniAdaptiveModel     0.500000   \n",
-       "4   VisaGold      None        4.0  Outbound  OmniAdaptiveModel     0.500000   \n",
-       "5   VisaGold      None        7.0  Outbound  OmniAdaptiveModel     0.500000   \n",
-       "6   VisaGold      None       12.0   Inbound  OmniAdaptiveModel     0.554538   \n",
-       "7   VisaGold      None       12.0   Inbound  OmniAdaptiveModel     0.554538   \n",
-       "8   VisaGold      None        8.0  Outbound  OmniAdaptiveModel     0.500000   \n",
-       "9   VisaGold      None       19.0   Inbound  OmniAdaptiveModel     0.541487   \n",
-       "10  VisaGold      None       10.0  Outbound  OmniAdaptiveModel     0.500000   \n",
-       "11  VisaGold      None       24.0   Inbound  OmniAdaptiveModel     0.557512   \n",
-       "12  VisaGold      None       28.0   Inbound  OmniAdaptiveModel     0.531173   \n",
-       "13  VisaGold      None       17.0  Outbound  OmniAdaptiveModel     0.551713   \n",
-       "14  VisaGold      None       17.0  Outbound  OmniAdaptiveModel     0.551713   \n",
-       "15  VisaGold      None       21.0  Outbound  OmniAdaptiveModel     0.524312   \n",
-       "16  VisaGold      None       39.0   Inbound  OmniAdaptiveModel     0.549843   \n",
-       "17  VisaGold      None       21.0  Outbound  OmniAdaptiveModel     0.524312   \n",
-       "18  VisaGold      None       24.0  Outbound  OmniAdaptiveModel     0.535621   \n",
-       "19  VisaGold      None       43.0   Inbound  OmniAdaptiveModel     0.538104   \n",
-       "20  VisaGold      None       27.0  Outbound  OmniAdaptiveModel     0.531863   \n",
-       "21  VisaGold      None       46.0   Inbound  OmniAdaptiveModel     0.519586   \n",
+       "                                 ModelID  Performance Direction Channel  \\\n",
+       "0   17571d69-f802-5122-9bfe-6f3f4ef9073b     0.500000   Inbound     Web   \n",
+       "1   1be7cd72-741b-5d68-b148-c1af0142fd4a     0.500000  Outbound     SMS   \n",
+       "2   17571d69-f802-5122-9bfe-6f3f4ef9073b     0.500000   Inbound     Web   \n",
+       "3   17571d69-f802-5122-9bfe-6f3f4ef9073b     0.500000   Inbound     Web   \n",
+       "4   1be7cd72-741b-5d68-b148-c1af0142fd4a     0.500000  Outbound     SMS   \n",
+       "5   1be7cd72-741b-5d68-b148-c1af0142fd4a     0.500000  Outbound     SMS   \n",
+       "6   17571d69-f802-5122-9bfe-6f3f4ef9073b     0.554538   Inbound     Web   \n",
+       "7   17571d69-f802-5122-9bfe-6f3f4ef9073b     0.554538   Inbound     Web   \n",
+       "8   1be7cd72-741b-5d68-b148-c1af0142fd4a     0.500000  Outbound     SMS   \n",
+       "9   17571d69-f802-5122-9bfe-6f3f4ef9073b     0.541487   Inbound     Web   \n",
+       "10  1be7cd72-741b-5d68-b148-c1af0142fd4a     0.500000  Outbound     SMS   \n",
+       "11  17571d69-f802-5122-9bfe-6f3f4ef9073b     0.557512   Inbound     Web   \n",
+       "12  17571d69-f802-5122-9bfe-6f3f4ef9073b     0.531173   Inbound     Web   \n",
+       "13  1be7cd72-741b-5d68-b148-c1af0142fd4a     0.551713  Outbound     SMS   \n",
+       "14  1be7cd72-741b-5d68-b148-c1af0142fd4a     0.551713  Outbound     SMS   \n",
+       "15  1be7cd72-741b-5d68-b148-c1af0142fd4a     0.524312  Outbound     SMS   \n",
+       "16  17571d69-f802-5122-9bfe-6f3f4ef9073b     0.549843   Inbound     Web   \n",
+       "17  1be7cd72-741b-5d68-b148-c1af0142fd4a     0.524312  Outbound     SMS   \n",
+       "18  1be7cd72-741b-5d68-b148-c1af0142fd4a     0.535621  Outbound     SMS   \n",
+       "19  17571d69-f802-5122-9bfe-6f3f4ef9073b     0.538104   Inbound     Web   \n",
+       "20  1be7cd72-741b-5d68-b148-c1af0142fd4a     0.531863  Outbound     SMS   \n",
+       "21  17571d69-f802-5122-9bfe-6f3f4ef9073b     0.519586   Inbound     Web   \n",
        "\n",
-       "              SnapshotTime  Issue  ResponseCount  \\\n",
-       "0  2021-06-01 08:23:26.789  Sales           72.0   \n",
-       "1  2021-06-01 08:23:26.789  Sales          166.0   \n",
-       "2  2021-06-01 08:27:55.789  Sales          161.0   \n",
-       "3  2021-06-01 09:03:01.789  Sales          161.0   \n",
-       "4  2021-06-01 09:03:01.789  Sales          166.0   \n",
-       "5  2021-06-01 09:06:33.789  Sales          271.0   \n",
-       "6  2021-06-01 09:06:33.789  Sales          233.0   \n",
-       "7  2021-06-01 09:34:34.789  Sales          233.0   \n",
-       "8  2021-06-01 09:43:16.789  Sales          353.0   \n",
-       "9  2021-06-01 09:43:16.789  Sales          320.0   \n",
-       "10 2021-06-01 10:07:35.789  Sales          437.0   \n",
-       "11 2021-06-01 10:07:35.789  Sales          391.0   \n",
-       "12 2021-06-01 10:58:26.789  Sales          470.0   \n",
-       "13 2021-06-01 10:58:26.789  Sales          507.0   \n",
-       "14 2021-06-01 11:29:54.789  Sales          507.0   \n",
-       "15 2021-06-01 12:15:05.789  Sales          674.0   \n",
-       "16 2021-06-01 12:15:05.789  Sales          633.0   \n",
-       "17 2021-06-01 12:42:04.789  Sales          674.0   \n",
-       "18 2021-06-01 12:45:20.789  Sales          746.0   \n",
-       "19 2021-06-01 13:18:21.789  Sales          706.0   \n",
-       "20 2021-06-01 13:23:26.789  Sales          822.0   \n",
-       "21 2021-06-01 13:23:26.789  Sales          778.0   \n",
+       "    ResponseCount      Configuration      Name            SnapshotTime  \\\n",
+       "0            72.0  OmniAdaptiveModel  VisaGold 2021-06-01 08:23:26.789   \n",
+       "1           166.0  OmniAdaptiveModel  VisaGold 2021-06-01 08:23:26.789   \n",
+       "2           161.0  OmniAdaptiveModel  VisaGold 2021-06-01 08:27:55.789   \n",
+       "3           161.0  OmniAdaptiveModel  VisaGold 2021-06-01 09:03:01.789   \n",
+       "4           166.0  OmniAdaptiveModel  VisaGold 2021-06-01 09:03:01.789   \n",
+       "5           271.0  OmniAdaptiveModel  VisaGold 2021-06-01 09:06:33.789   \n",
+       "6           233.0  OmniAdaptiveModel  VisaGold 2021-06-01 09:06:33.789   \n",
+       "7           233.0  OmniAdaptiveModel  VisaGold 2021-06-01 09:34:34.789   \n",
+       "8           353.0  OmniAdaptiveModel  VisaGold 2021-06-01 09:43:16.789   \n",
+       "9           320.0  OmniAdaptiveModel  VisaGold 2021-06-01 09:43:16.789   \n",
+       "10          437.0  OmniAdaptiveModel  VisaGold 2021-06-01 10:07:35.789   \n",
+       "11          391.0  OmniAdaptiveModel  VisaGold 2021-06-01 10:07:35.789   \n",
+       "12          470.0  OmniAdaptiveModel  VisaGold 2021-06-01 10:58:26.789   \n",
+       "13          507.0  OmniAdaptiveModel  VisaGold 2021-06-01 10:58:26.789   \n",
+       "14          507.0  OmniAdaptiveModel  VisaGold 2021-06-01 11:29:54.789   \n",
+       "15          674.0  OmniAdaptiveModel  VisaGold 2021-06-01 12:15:05.789   \n",
+       "16          633.0  OmniAdaptiveModel  VisaGold 2021-06-01 12:15:05.789   \n",
+       "17          674.0  OmniAdaptiveModel  VisaGold 2021-06-01 12:42:04.789   \n",
+       "18          746.0  OmniAdaptiveModel  VisaGold 2021-06-01 12:45:20.789   \n",
+       "19          706.0  OmniAdaptiveModel  VisaGold 2021-06-01 13:18:21.789   \n",
+       "20          822.0  OmniAdaptiveModel  VisaGold 2021-06-01 13:23:26.789   \n",
+       "21          778.0  OmniAdaptiveModel  VisaGold 2021-06-01 13:23:26.789   \n",
        "\n",
-       "                                 ModelID        Group Channel  SuccessRate  \\\n",
-       "0   17571d69-f802-5122-9bfe-6f3f4ef9073b  CreditCards     Web     0.013889   \n",
-       "1   1be7cd72-741b-5d68-b148-c1af0142fd4a  CreditCards     SMS     0.024096   \n",
-       "2   17571d69-f802-5122-9bfe-6f3f4ef9073b  CreditCards     Web     0.018634   \n",
-       "3   17571d69-f802-5122-9bfe-6f3f4ef9073b  CreditCards     Web     0.018634   \n",
-       "4   1be7cd72-741b-5d68-b148-c1af0142fd4a  CreditCards     SMS     0.024096   \n",
-       "5   1be7cd72-741b-5d68-b148-c1af0142fd4a  CreditCards     SMS     0.025830   \n",
-       "6   17571d69-f802-5122-9bfe-6f3f4ef9073b  CreditCards     Web     0.051502   \n",
-       "7   17571d69-f802-5122-9bfe-6f3f4ef9073b  CreditCards     Web     0.051502   \n",
-       "8   1be7cd72-741b-5d68-b148-c1af0142fd4a  CreditCards     SMS     0.022663   \n",
-       "9   17571d69-f802-5122-9bfe-6f3f4ef9073b  CreditCards     Web     0.059375   \n",
-       "10  1be7cd72-741b-5d68-b148-c1af0142fd4a  CreditCards     SMS     0.022883   \n",
-       "11  17571d69-f802-5122-9bfe-6f3f4ef9073b  CreditCards     Web     0.061381   \n",
-       "12  17571d69-f802-5122-9bfe-6f3f4ef9073b  CreditCards     Web     0.059574   \n",
-       "13  1be7cd72-741b-5d68-b148-c1af0142fd4a  CreditCards     SMS     0.033531   \n",
-       "14  1be7cd72-741b-5d68-b148-c1af0142fd4a  CreditCards     SMS     0.033531   \n",
-       "15  1be7cd72-741b-5d68-b148-c1af0142fd4a  CreditCards     SMS     0.031157   \n",
-       "16  17571d69-f802-5122-9bfe-6f3f4ef9073b  CreditCards     Web     0.061611   \n",
-       "17  1be7cd72-741b-5d68-b148-c1af0142fd4a  CreditCards     SMS     0.031157   \n",
-       "18  1be7cd72-741b-5d68-b148-c1af0142fd4a  CreditCards     SMS     0.032172   \n",
-       "19  17571d69-f802-5122-9bfe-6f3f4ef9073b  CreditCards     Web     0.060907   \n",
-       "20  1be7cd72-741b-5d68-b148-c1af0142fd4a  CreditCards     SMS     0.032847   \n",
-       "21  17571d69-f802-5122-9bfe-6f3f4ef9073b  CreditCards     Web     0.059126   \n",
+       "   Treatment  Positives        Group  Issue  SuccessRate  \\\n",
+       "0       None        1.0  CreditCards  Sales     0.013889   \n",
+       "1       None        4.0  CreditCards  Sales     0.024096   \n",
+       "2       None        3.0  CreditCards  Sales     0.018634   \n",
+       "3       None        3.0  CreditCards  Sales     0.018634   \n",
+       "4       None        4.0  CreditCards  Sales     0.024096   \n",
+       "5       None        7.0  CreditCards  Sales     0.025830   \n",
+       "6       None       12.0  CreditCards  Sales     0.051502   \n",
+       "7       None       12.0  CreditCards  Sales     0.051502   \n",
+       "8       None        8.0  CreditCards  Sales     0.022663   \n",
+       "9       None       19.0  CreditCards  Sales     0.059375   \n",
+       "10      None       10.0  CreditCards  Sales     0.022883   \n",
+       "11      None       24.0  CreditCards  Sales     0.061381   \n",
+       "12      None       28.0  CreditCards  Sales     0.059574   \n",
+       "13      None       17.0  CreditCards  Sales     0.033531   \n",
+       "14      None       17.0  CreditCards  Sales     0.033531   \n",
+       "15      None       21.0  CreditCards  Sales     0.031157   \n",
+       "16      None       39.0  CreditCards  Sales     0.061611   \n",
+       "17      None       21.0  CreditCards  Sales     0.031157   \n",
+       "18      None       24.0  CreditCards  Sales     0.032172   \n",
+       "19      None       43.0  CreditCards  Sales     0.060907   \n",
+       "20      None       27.0  CreditCards  Sales     0.032847   \n",
+       "21      None       46.0  CreditCards  Sales     0.059126   \n",
        "\n",
        "            Last_Positives      Last_ResponseCount  \n",
        "0  2021-06-01 13:23:26.789 2021-06-01 13:23:26.789  \n",
@@ -1037,10 +1047,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:14.964779Z",
-     "iopub.status.busy": "2024-06-27T08:58:14.964423Z",
-     "iopub.status.idle": "2024-06-27T08:58:14.982958Z",
-     "shell.execute_reply": "2024-06-27T08:58:14.982425Z"
+     "iopub.execute_input": "2024-06-28T08:59:32.083611Z",
+     "iopub.status.busy": "2024-06-28T08:59:32.083277Z",
+     "iopub.status.idle": "2024-06-28T08:59:32.101701Z",
+     "shell.execute_reply": "2024-06-28T08:59:32.101169Z"
     }
    },
    "outputs": [
@@ -1125,7 +1135,7 @@
        "<g id=\"node6\" class=\"node\">\n",
        "<title>p6</title>\n",
        "<polygon fill=\"none\" stroke=\"black\" points=\"4810,-430 0,-430 0,-394 4810,-394 4810,-430\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2405\" y=\"-408.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(&#45;))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical))]</text>\n",
+       "<text text-anchor=\"middle\" x=\"2405\" y=\"-408.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(&#45;))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical))]</text>\n",
        "</g>\n",
        "<!-- p5&#45;&#45;p6 -->\n",
        "<g id=\"edge5\" class=\"edge\">\n",
@@ -1217,7 +1227,7 @@
        "<g id=\"node14\" class=\"node\">\n",
        "<title>p14</title>\n",
        "<polygon fill=\"none\" stroke=\"black\" points=\"10714,-430 4828,-430 4828,-394 10714,-394 10714,-430\"/>\n",
-       "<text text-anchor=\"middle\" x=\"7771\" y=\"-408.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [col(&quot;BinPositives&quot;).strict_cast(Float32), col(&quot;GroupIndex&quot;).strict_cast(UInt32), col(&quot;PredictorName&quot;).strict_cast(Categorical(None, Physical)), col(&quot;BinType&quot;).strict_cast(Categorical(None, Physical)), col(&quot;BinIndex&quot;).strict_cast(UInt16), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;BinResponseCount&quot;).strict_cast(Float32), col(&quot;Type&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(&#45;))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;BinNegatives&quot;).strict_cast(Float32), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;EntryType&quot;).strict_cast(Categorical(None, Physical))]</text>\n",
+       "<text text-anchor=\"middle\" x=\"7771\" y=\"-408.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [col(&quot;BinResponseCount&quot;).strict_cast(Float32), col(&quot;GroupIndex&quot;).strict_cast(UInt32), col(&quot;PredictorName&quot;).strict_cast(Categorical(None, Physical)), col(&quot;BinIndex&quot;).strict_cast(UInt16), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;BinNegatives&quot;).strict_cast(Float32), col(&quot;EntryType&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Type&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(&#45;))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;BinType&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;BinPositives&quot;).strict_cast(Float32)]</text>\n",
        "</g>\n",
        "<!-- p13&#45;&#45;p14 -->\n",
        "<g id=\"edge13\" class=\"edge\">\n",
@@ -1296,7 +1306,7 @@
        "<g id=\"node22\" class=\"node\">\n",
        "<title>p22</title>\n",
        "<polygon fill=\"none\" stroke=\"black\" points=\"15589,-286 10779,-286 10779,-250 15589,-250 15589,-286\"/>\n",
-       "<text text-anchor=\"middle\" x=\"13184\" y=\"-264.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(&#45;))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical))]</text>\n",
+       "<text text-anchor=\"middle\" x=\"13184\" y=\"-264.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(&#45;))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical))]</text>\n",
        "</g>\n",
        "<!-- p21&#45;&#45;p22 -->\n",
        "<g id=\"edge21\" class=\"edge\">\n",
@@ -1342,7 +1352,7 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<LazyFrame at 0x7F9611BCAA10>"
+       "<LazyFrame at 0x7F3550C04F90>"
       ]
      },
      "execution_count": 8,
@@ -1369,283 +1379,283 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:14.984743Z",
-     "iopub.status.busy": "2024-06-27T08:58:14.984580Z",
-     "iopub.status.idle": "2024-06-27T08:58:15.006669Z",
-     "shell.execute_reply": "2024-06-27T08:58:15.006144Z"
+     "iopub.execute_input": "2024-06-28T08:59:32.103642Z",
+     "iopub.status.busy": "2024-06-28T08:59:32.103353Z",
+     "iopub.status.idle": "2024-06-28T08:59:32.125467Z",
+     "shell.execute_reply": "2024-06-28T08:59:32.124935Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"11280pt\" height=\"906pt\" viewBox=\"0.00 0.00 11280.00 906.00\">\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"11238pt\" height=\"906pt\" viewBox=\"0.00 0.00 11238.00 906.00\">\n",
        "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 902)\">\n",
        "<title>polars_query</title>\n",
-       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-902 11276,-902 11276,4 -4,4\"/>\n",
+       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-902 11234,-902 11234,4 -4,4\"/>\n",
        "<!-- p1 -->\n",
        "<g id=\"node1\" class=\"node\">\n",
        "<title>p1</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"5243.5,-898 5067.5,-898 5067.5,-845 5243.5,-845 5243.5,-898\"/>\n",
-       "<text text-anchor=\"middle\" x=\"5155.5\" y=\"-882.8\" font-family=\"Times,serif\" font-size=\"14.00\">JOIN INNER</text>\n",
-       "<text text-anchor=\"middle\" x=\"5155.5\" y=\"-867.8\" font-family=\"Times,serif\" font-size=\"14.00\">left: [col(&quot;ModelID&quot;)];</text>\n",
-       "<text text-anchor=\"middle\" x=\"5155.5\" y=\"-852.8\" font-family=\"Times,serif\" font-size=\"14.00\">right: [col(&quot;ModelID&quot;)]</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"5217,-898 5041,-898 5041,-845 5217,-845 5217,-898\"/>\n",
+       "<text text-anchor=\"middle\" x=\"5129\" y=\"-882.8\" font-family=\"Times,serif\" font-size=\"14.00\">JOIN INNER</text>\n",
+       "<text text-anchor=\"middle\" x=\"5129\" y=\"-867.8\" font-family=\"Times,serif\" font-size=\"14.00\">left: [col(&quot;ModelID&quot;)];</text>\n",
+       "<text text-anchor=\"middle\" x=\"5129\" y=\"-852.8\" font-family=\"Times,serif\" font-size=\"14.00\">right: [col(&quot;ModelID&quot;)]</text>\n",
        "</g>\n",
        "<!-- p2 -->\n",
        "<g id=\"node2\" class=\"node\">\n",
        "<title>p2</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"5314,-728.5 4471,-728.5 4471,-692.5 5314,-692.5 5314,-728.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"4892.5\" y=\"-706.8\" font-family=\"Times,serif\" font-size=\"14.00\">FILTER BY [(col(&quot;SnapshotTime&quot;).fill_null_with_strategy()) == (col(&quot;SnapshotTime&quot;).fill_null_with_strategy().max())]</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"5287.5,-728.5 4444.5,-728.5 4444.5,-692.5 5287.5,-692.5 5287.5,-728.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"4866\" y=\"-706.8\" font-family=\"Times,serif\" font-size=\"14.00\">FILTER BY [(col(&quot;SnapshotTime&quot;).fill_null_with_strategy()) == (col(&quot;SnapshotTime&quot;).fill_null_with_strategy().max())]</text>\n",
        "</g>\n",
        "<!-- p1&#45;&#45;p2 -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
        "<title>p1--p2</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M5067.29,-852.66C5034.61,-843.24 4998.48,-829.29 4969.5,-809 4938.93,-787.6 4914.51,-750.6 4901.89,-728.85\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5040.79,-852.66C5008.11,-843.24 4971.98,-829.29 4943,-809 4912.43,-787.6 4888.01,-750.6 4875.39,-728.85\"/>\n",
        "</g>\n",
        "<!-- p9 -->\n",
        "<g id=\"node10\" class=\"node\">\n",
        "<title>p9</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"5821,-809 4978,-809 4978,-773 5821,-773 5821,-809\"/>\n",
-       "<text text-anchor=\"middle\" x=\"5399.5\" y=\"-787.3\" font-family=\"Times,serif\" font-size=\"14.00\">FILTER BY [(col(&quot;SnapshotTime&quot;).fill_null_with_strategy()) == (col(&quot;SnapshotTime&quot;).fill_null_with_strategy().max())]</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"5794.5,-809 4951.5,-809 4951.5,-773 5794.5,-773 5794.5,-809\"/>\n",
+       "<text text-anchor=\"middle\" x=\"5373\" y=\"-787.3\" font-family=\"Times,serif\" font-size=\"14.00\">FILTER BY [(col(&quot;SnapshotTime&quot;).fill_null_with_strategy()) == (col(&quot;SnapshotTime&quot;).fill_null_with_strategy().max())]</text>\n",
        "</g>\n",
        "<!-- p1&#45;&#45;p9 -->\n",
        "<g id=\"edge9\" class=\"edge\">\n",
        "<title>p1--p9</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M5235.06,-844.9C5271.63,-833.14 5314.02,-819.5 5346.6,-809.02\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5208.56,-844.9C5245.13,-833.14 5287.52,-819.5 5320.1,-809.02\"/>\n",
        "</g>\n",
        "<!-- c0 -->\n",
        "<g id=\"node3\" class=\"node\">\n",
        "<title>c0</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"5001.5,-502 4869.5,-502 4869.5,-466 5001.5,-466 5001.5,-502\"/>\n",
-       "<text text-anchor=\"middle\" x=\"4935.5\" y=\"-480.3\" font-family=\"Times,serif\" font-size=\"14.00\">CACHE: 1 times</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"4975,-502 4843,-502 4843,-466 4975,-466 4975,-502\"/>\n",
+       "<text text-anchor=\"middle\" x=\"4909\" y=\"-480.3\" font-family=\"Times,serif\" font-size=\"14.00\">CACHE: 1 times</text>\n",
        "</g>\n",
        "<!-- p2&#45;&#45;c0 -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
        "<title>p2--c0</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M4892.63,-692.13C4893.27,-660.77 4896.44,-592.89 4911.5,-538 4914.92,-525.53 4920.92,-512.28 4926.07,-502.15\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M4866.13,-692.13C4866.77,-660.77 4869.94,-592.89 4885,-538 4888.42,-525.53 4894.42,-512.28 4899.57,-502.15\"/>\n",
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        "<!-- p3 -->\n",
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        "<title>p3</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"6532.5,-421.5 6154.5,-421.5 6154.5,-385.5 6532.5,-385.5 6532.5,-421.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"6343.5\" y=\"-399.8\" font-family=\"Times,serif\" font-size=\"14.00\">FILTER BY col(&quot;Name&quot;).str.contains([String(Visa)])</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"6506,-421.5 6128,-421.5 6128,-385.5 6506,-385.5 6506,-421.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"6317\" y=\"-399.8\" font-family=\"Times,serif\" font-size=\"14.00\">FILTER BY col(&quot;Name&quot;).str.contains([String(Visa)])</text>\n",
        "</g>\n",
        "<!-- c0&#45;&#45;p3 -->\n",
        "<g id=\"edge3\" class=\"edge\">\n",
        "<title>c0--p3</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M5001.52,-479.32C5206.22,-467.91 5835.53,-432.82 6154.29,-415.05\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M4975.02,-479.32C5179.72,-467.91 5809.03,-432.82 6127.79,-415.05\"/>\n",
        "</g>\n",
        "<!-- p17 -->\n",
        "<g id=\"node18\" class=\"node\">\n",
        "<title>p17</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"3709.5,-421.5 3331.5,-421.5 3331.5,-385.5 3709.5,-385.5 3709.5,-421.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"3520.5\" y=\"-399.8\" font-family=\"Times,serif\" font-size=\"14.00\">FILTER BY col(&quot;Name&quot;).str.contains([String(Visa)])</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"3694,-421.5 3316,-421.5 3316,-385.5 3694,-385.5 3694,-421.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"3505\" y=\"-399.8\" font-family=\"Times,serif\" font-size=\"14.00\">FILTER BY col(&quot;Name&quot;).str.contains([String(Visa)])</text>\n",
        "</g>\n",
        "<!-- c0&#45;&#45;p17 -->\n",
        "<g id=\"edge18\" class=\"edge\">\n",
        "<title>c0--p17</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M4869.15,-479.32C4663.23,-467.9 4029.73,-432.75 3709.7,-415\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M4842.7,-479.29C4638.19,-467.86 4011.86,-432.84 3694.12,-415.07\"/>\n",
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        "<!-- p4 -->\n",
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        "<title>p4</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"11272,-341 5645,-341 5645,-305 11272,-305 11272,-341\"/>\n",
-       "<text text-anchor=\"middle\" x=\"8458.5\" y=\"-319.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [when([(col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;).is_not_nan()) | (col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;).is_null())]).then(col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;)).otherwise(0.0).alias(&quot;SuccessRate&quot;), when([(col(&quot;Positives&quot;).min()) == (col(&quot;Positives&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;Positives&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_Positives&quot;), when([(col(&quot;ResponseCount&quot;).min()) == (col(&quot;ResponseCount&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;ResponseCount&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_ResponseCount&quot;), [(col(&quot;Positives&quot;)) / (col(&quot;ResponseCount&quot;))].alias(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;)]</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"11230,-341 5624,-341 5624,-305 11230,-305 11230,-341\"/>\n",
+       "<text text-anchor=\"middle\" x=\"8427\" y=\"-319.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [when([(col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;).is_not_nan()) | (col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;).is_null())]).then(col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;)).otherwise(0.0).alias(&quot;SuccessRate&quot;), when([(col(&quot;Positives&quot;).min()) == (col(&quot;Positives&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;Positives&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_Positives&quot;), when([(col(&quot;ResponseCount&quot;).min()) == (col(&quot;ResponseCount&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;ResponseCount&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_ResponseCount&quot;), [(col(&quot;Positives&quot;)) / (col(&quot;ResponseCount&quot;))].alias(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;)]</text>\n",
        "</g>\n",
        "<!-- p3&#45;&#45;p4 -->\n",
        "<g id=\"edge4\" class=\"edge\">\n",
        "<title>p3--p4</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M6532.59,-395.48C6860.23,-383.32 7542.26,-358.01 8000.32,-341.01\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6506.11,-395.46C6833.14,-383.3 7513.12,-358 7969.88,-341.01\"/>\n",
        "</g>\n",
        "<!-- p5 -->\n",
        "<g id=\"node6\" class=\"node\">\n",
        "<title>p5</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"10863.5,-269 6053.5,-269 6053.5,-233 10863.5,-233 10863.5,-269\"/>\n",
-       "<text text-anchor=\"middle\" x=\"8458.5\" y=\"-247.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical))]</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"10832,-269 6022,-269 6022,-233 10832,-233 10832,-269\"/>\n",
+       "<text text-anchor=\"middle\" x=\"8427\" y=\"-247.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical))]</text>\n",
        "</g>\n",
        "<!-- p4&#45;&#45;p5 -->\n",
        "<g id=\"edge5\" class=\"edge\">\n",
        "<title>p4--p5</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M8458.5,-304.7C8458.5,-293.85 8458.5,-279.92 8458.5,-269.1\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M8427,-304.7C8427,-293.85 8427,-279.92 8427,-269.1\"/>\n",
        "</g>\n",
        "<!-- p6 -->\n",
        "<g id=\"node7\" class=\"node\">\n",
        "<title>p6</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"8494,-197 8423,-197 8423,-161 8494,-161 8494,-197\"/>\n",
-       "<text text-anchor=\"middle\" x=\"8458.5\" y=\"-175.3\" font-family=\"Times,serif\" font-size=\"14.00\">π 12/12</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"8462.5,-197 8391.5,-197 8391.5,-161 8462.5,-161 8462.5,-197\"/>\n",
+       "<text text-anchor=\"middle\" x=\"8427\" y=\"-175.3\" font-family=\"Times,serif\" font-size=\"14.00\">π 12/12</text>\n",
        "</g>\n",
        "<!-- p5&#45;&#45;p6 -->\n",
        "<g id=\"edge6\" class=\"edge\">\n",
        "<title>p5--p6</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M8458.5,-232.7C8458.5,-221.85 8458.5,-207.92 8458.5,-197.1\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M8427,-232.7C8427,-221.85 8427,-207.92 8427,-197.1\"/>\n",
        "</g>\n",
        "<!-- p7 -->\n",
        "<g id=\"node8\" class=\"node\">\n",
        "<title>p7</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"8500.5,-125 8416.5,-125 8416.5,-89 8500.5,-89 8500.5,-125\"/>\n",
-       "<text text-anchor=\"middle\" x=\"8458.5\" y=\"-103.3\" font-family=\"Times,serif\" font-size=\"14.00\">RENAME</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"8469,-125 8385,-125 8385,-89 8469,-89 8469,-125\"/>\n",
+       "<text text-anchor=\"middle\" x=\"8427\" y=\"-103.3\" font-family=\"Times,serif\" font-size=\"14.00\">RENAME</text>\n",
        "</g>\n",
        "<!-- p6&#45;&#45;p7 -->\n",
        "<g id=\"edge7\" class=\"edge\">\n",
        "<title>p6--p7</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M8458.5,-160.7C8458.5,-149.85 8458.5,-135.92 8458.5,-125.1\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M8427,-160.7C8427,-149.85 8427,-135.92 8427,-125.1\"/>\n",
        "</g>\n",
        "<!-- p8 -->\n",
        "<g id=\"node9\" class=\"node\">\n",
        "<title>p8</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"8614,-53 8303,-53 8303,0 8614,0 8614,-53\"/>\n",
-       "<text text-anchor=\"middle\" x=\"8458.5\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">TABLE</text>\n",
-       "<text text-anchor=\"middle\" x=\"8458.5\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">π 12/27;</text>\n",
-       "<text text-anchor=\"middle\" x=\"8458.5\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">σ col(&quot;Name&quot;).str.contains([String(Visa)])</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"8582.5,-53 8271.5,-53 8271.5,0 8582.5,0 8582.5,-53\"/>\n",
+       "<text text-anchor=\"middle\" x=\"8427\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">TABLE</text>\n",
+       "<text text-anchor=\"middle\" x=\"8427\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">π 12/27;</text>\n",
+       "<text text-anchor=\"middle\" x=\"8427\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">σ col(&quot;Name&quot;).str.contains([String(Visa)])</text>\n",
        "</g>\n",
        "<!-- p7&#45;&#45;p8 -->\n",
        "<g id=\"edge8\" class=\"edge\">\n",
        "<title>p7--p8</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M8458.5,-88.97C8458.5,-78.51 8458.5,-64.9 8458.5,-53.16\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M8427,-88.97C8427,-78.51 8427,-64.9 8427,-53.16\"/>\n",
        "</g>\n",
        "<!-- p10 -->\n",
        "<g id=\"node11\" class=\"node\">\n",
        "<title>p10</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"5507.5,-737 5331.5,-737 5331.5,-684 5507.5,-684 5507.5,-737\"/>\n",
-       "<text text-anchor=\"middle\" x=\"5419.5\" y=\"-721.8\" font-family=\"Times,serif\" font-size=\"14.00\">JOIN INNER</text>\n",
-       "<text text-anchor=\"middle\" x=\"5419.5\" y=\"-706.8\" font-family=\"Times,serif\" font-size=\"14.00\">left: [col(&quot;ModelID&quot;)];</text>\n",
-       "<text text-anchor=\"middle\" x=\"5419.5\" y=\"-691.8\" font-family=\"Times,serif\" font-size=\"14.00\">right: [col(&quot;ModelID&quot;)]</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"5481,-737 5305,-737 5305,-684 5481,-684 5481,-737\"/>\n",
+       "<text text-anchor=\"middle\" x=\"5393\" y=\"-721.8\" font-family=\"Times,serif\" font-size=\"14.00\">JOIN INNER</text>\n",
+       "<text text-anchor=\"middle\" x=\"5393\" y=\"-706.8\" font-family=\"Times,serif\" font-size=\"14.00\">left: [col(&quot;ModelID&quot;)];</text>\n",
+       "<text text-anchor=\"middle\" x=\"5393\" y=\"-691.8\" font-family=\"Times,serif\" font-size=\"14.00\">right: [col(&quot;ModelID&quot;)]</text>\n",
        "</g>\n",
        "<!-- p9&#45;&#45;p10 -->\n",
        "<g id=\"edge10\" class=\"edge\">\n",
        "<title>p9--p10</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M5403.84,-772.97C5406.51,-762.51 5409.97,-748.9 5412.96,-737.16\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5377.34,-772.97C5380.01,-762.51 5383.47,-748.9 5386.46,-737.16\"/>\n",
        "</g>\n",
        "<!-- p11 -->\n",
        "<g id=\"node12\" class=\"node\">\n",
        "<title>p11</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"9471.5,-648 5815.5,-648 5815.5,-612 9471.5,-612 9471.5,-648\"/>\n",
-       "<text text-anchor=\"middle\" x=\"7643.5\" y=\"-626.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [[(col(&quot;BinPositives&quot;)) / (col(&quot;BinResponseCount&quot;))].alias(&quot;BinPropensity&quot;), [([(col(&quot;BinPositives&quot;)) + (0.5)]) / ([(col(&quot;BinResponseCount&quot;)) + (1.0)])].alias(&quot;BinAdjustedPropensity&quot;), when([(col(&quot;__POLARS_CSER_0x3914851859250ee2&quot;).list.length()) &gt; (1)]).then(col(&quot;__POLARS_CSER_0x3914851859250ee2&quot;).list.get([dyn int: 0])).otherwise(String(Primary)).alias(&quot;PredictorCategory&quot;), col(&quot;PredictorName&quot;).strict_cast(String).str.split([String(.)]).alias(&quot;__POLARS_CSER_0x3914851859250ee2&quot;)]</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"9438.5,-648 5791.5,-648 5791.5,-612 9438.5,-612 9438.5,-648\"/>\n",
+       "<text text-anchor=\"middle\" x=\"7615\" y=\"-626.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [[(col(&quot;BinPositives&quot;)) / (col(&quot;BinResponseCount&quot;))].alias(&quot;BinPropensity&quot;), [([(col(&quot;BinPositives&quot;)) + (0.5)]) / ([(col(&quot;BinResponseCount&quot;)) + (1.0)])].alias(&quot;BinAdjustedPropensity&quot;), when([(col(&quot;__POLARS_CSER_0x96a44e70932c0d7c&quot;).list.length()) &gt; (1)]).then(col(&quot;__POLARS_CSER_0x96a44e70932c0d7c&quot;).list.get([dyn int: 0])).otherwise(String(Primary)).alias(&quot;PredictorCategory&quot;), col(&quot;PredictorName&quot;).strict_cast(String).str.split([String(.)]).alias(&quot;__POLARS_CSER_0x96a44e70932c0d7c&quot;)]</text>\n",
        "</g>\n",
        "<!-- p10&#45;&#45;p11 -->\n",
        "<g id=\"edge11\" class=\"edge\">\n",
        "<title>p10--p11</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M5507.71,-706.39C5779.72,-696.79 6619.77,-667.13 7161.71,-648.01\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5481.13,-706.39C5752.89,-696.79 6592.2,-667.13 7133.64,-648.01\"/>\n",
        "</g>\n",
        "<!-- p15 -->\n",
        "<g id=\"node16\" class=\"node\">\n",
        "<title>p15</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"5225.5,-648 5171.5,-648 5171.5,-612 5225.5,-612 5225.5,-648\"/>\n",
-       "<text text-anchor=\"middle\" x=\"5198.5\" y=\"-626.3\" font-family=\"Times,serif\" font-size=\"14.00\">π 1/1</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"5199,-648 5145,-648 5145,-612 5199,-612 5199,-648\"/>\n",
+       "<text text-anchor=\"middle\" x=\"5172\" y=\"-626.3\" font-family=\"Times,serif\" font-size=\"14.00\">π 1/1</text>\n",
        "</g>\n",
        "<!-- p10&#45;&#45;p15 -->\n",
        "<g id=\"edge15\" class=\"edge\">\n",
        "<title>p10--p15</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M5347.44,-683.9C5305.75,-669.1 5255.71,-651.32 5225.55,-640.61\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5320.94,-683.9C5279.25,-669.1 5229.21,-651.32 5199.05,-640.61\"/>\n",
        "</g>\n",
        "<!-- p12 -->\n",
        "<g id=\"node13\" class=\"node\">\n",
        "<title>p12</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"10938.5,-575 5052.5,-575 5052.5,-539 10938.5,-539 10938.5,-575\"/>\n",
-       "<text text-anchor=\"middle\" x=\"7995.5\" y=\"-553.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [col(&quot;BinPositives&quot;).strict_cast(Float32), col(&quot;GroupIndex&quot;).strict_cast(UInt32), col(&quot;PredictorName&quot;).strict_cast(Categorical(None, Physical)), col(&quot;BinType&quot;).strict_cast(Categorical(None, Physical)), col(&quot;BinIndex&quot;).strict_cast(UInt16), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;BinResponseCount&quot;).strict_cast(Float32), col(&quot;Type&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;BinNegatives&quot;).strict_cast(Float32), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;EntryType&quot;).strict_cast(Categorical(None, Physical))]</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"10912,-575 5026,-575 5026,-539 10912,-539 10912,-575\"/>\n",
+       "<text text-anchor=\"middle\" x=\"7969\" y=\"-553.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [col(&quot;BinResponseCount&quot;).strict_cast(Float32), col(&quot;GroupIndex&quot;).strict_cast(UInt32), col(&quot;PredictorName&quot;).strict_cast(Categorical(None, Physical)), col(&quot;BinIndex&quot;).strict_cast(UInt16), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;BinNegatives&quot;).strict_cast(Float32), col(&quot;EntryType&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Type&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;BinType&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;BinPositives&quot;).strict_cast(Float32)]</text>\n",
        "</g>\n",
        "<!-- p11&#45;&#45;p12 -->\n",
        "<g id=\"edge12\" class=\"edge\">\n",
        "<title>p11--p12</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M7727.81,-611.99C7783.24,-600.81 7855.49,-586.24 7910.96,-575.05\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M7699.79,-611.99C7755.53,-600.81 7828.2,-586.24 7883.98,-575.05\"/>\n",
        "</g>\n",
        "<!-- p13 -->\n",
        "<g id=\"node14\" class=\"node\">\n",
        "<title>p13</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"8037.5,-502 7953.5,-502 7953.5,-466 8037.5,-466 8037.5,-502\"/>\n",
-       "<text text-anchor=\"middle\" x=\"7995.5\" y=\"-480.3\" font-family=\"Times,serif\" font-size=\"14.00\">RENAME</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"8011,-502 7927,-502 7927,-466 8011,-466 8011,-502\"/>\n",
+       "<text text-anchor=\"middle\" x=\"7969\" y=\"-480.3\" font-family=\"Times,serif\" font-size=\"14.00\">RENAME</text>\n",
        "</g>\n",
        "<!-- p12&#45;&#45;p13 -->\n",
        "<g id=\"edge13\" class=\"edge\">\n",
        "<title>p12--p13</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M7995.5,-538.81C7995.5,-527.65 7995.5,-513.16 7995.5,-502.03\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M7969,-538.81C7969,-527.65 7969,-513.16 7969,-502.03\"/>\n",
        "</g>\n",
        "<!-- p14 -->\n",
        "<g id=\"node15\" class=\"node\">\n",
        "<title>p14</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"8033,-430 7958,-430 7958,-377 8033,-377 8033,-430\"/>\n",
-       "<text text-anchor=\"middle\" x=\"7995.5\" y=\"-414.8\" font-family=\"Times,serif\" font-size=\"14.00\">TABLE</text>\n",
-       "<text text-anchor=\"middle\" x=\"7995.5\" y=\"-399.8\" font-family=\"Times,serif\" font-size=\"14.00\">π 16/36;</text>\n",
-       "<text text-anchor=\"middle\" x=\"7995.5\" y=\"-384.8\" font-family=\"Times,serif\" font-size=\"14.00\">σ None</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"8006.5,-430 7931.5,-430 7931.5,-377 8006.5,-377 8006.5,-430\"/>\n",
+       "<text text-anchor=\"middle\" x=\"7969\" y=\"-414.8\" font-family=\"Times,serif\" font-size=\"14.00\">TABLE</text>\n",
+       "<text text-anchor=\"middle\" x=\"7969\" y=\"-399.8\" font-family=\"Times,serif\" font-size=\"14.00\">π 16/36;</text>\n",
+       "<text text-anchor=\"middle\" x=\"7969\" y=\"-384.8\" font-family=\"Times,serif\" font-size=\"14.00\">σ None</text>\n",
        "</g>\n",
        "<!-- p13&#45;&#45;p14 -->\n",
        "<g id=\"edge14\" class=\"edge\">\n",
        "<title>p13--p14</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M7995.5,-465.97C7995.5,-455.51 7995.5,-441.9 7995.5,-430.16\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M7969,-465.97C7969,-455.51 7969,-441.9 7969,-430.16\"/>\n",
        "</g>\n",
        "<!-- p16 -->\n",
        "<g id=\"node17\" class=\"node\">\n",
        "<title>p16</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"5034,-576 4921,-576 4921,-538 5034,-538 5034,-576\"/>\n",
-       "<text text-anchor=\"middle\" x=\"4977.5\" y=\"-560.8\" font-family=\"Times,serif\" font-size=\"14.00\">simple π 1/15</text>\n",
-       "<text text-anchor=\"middle\" x=\"4977.5\" y=\"-545.8\" font-family=\"Times,serif\" font-size=\"14.00\">[&quot;ModelID&quot;]</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"5007.5,-576 4894.5,-576 4894.5,-538 5007.5,-538 5007.5,-576\"/>\n",
+       "<text text-anchor=\"middle\" x=\"4951\" y=\"-560.8\" font-family=\"Times,serif\" font-size=\"14.00\">simple π 1/15</text>\n",
+       "<text text-anchor=\"middle\" x=\"4951\" y=\"-545.8\" font-family=\"Times,serif\" font-size=\"14.00\">[&quot;ModelID&quot;]</text>\n",
        "</g>\n",
        "<!-- p15&#45;&#45;p16 -->\n",
        "<g id=\"edge16\" class=\"edge\">\n",
        "<title>p15--p16</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M5171.4,-620.29C5137.28,-609.33 5077.81,-590.23 5033.7,-576.05\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5144.9,-620.29C5110.78,-609.33 5051.31,-590.23 5007.2,-576.05\"/>\n",
        "</g>\n",
        "<!-- p16&#45;&#45;c0 -->\n",
        "<g id=\"edge17\" class=\"edge\">\n",
        "<title>p16--c0</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M4966.68,-537.72C4960.17,-526.7 4951.93,-512.78 4945.56,-502\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M4940.18,-537.72C4933.67,-526.7 4925.43,-512.78 4919.06,-502\"/>\n",
        "</g>\n",
        "<!-- p18 -->\n",
        "<g id=\"node19\" class=\"node\">\n",
        "<title>p18</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"5627,-341 0,-341 0,-305 5627,-305 5627,-341\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-319.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [when([(col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;).is_not_nan()) | (col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;).is_null())]).then(col(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;)).otherwise(0.0).alias(&quot;SuccessRate&quot;), when([(col(&quot;Positives&quot;).min()) == (col(&quot;Positives&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;Positives&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_Positives&quot;), when([(col(&quot;ResponseCount&quot;).min()) == (col(&quot;ResponseCount&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;ResponseCount&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_ResponseCount&quot;), [(col(&quot;Positives&quot;)) / (col(&quot;ResponseCount&quot;))].alias(&quot;__POLARS_CSER_0xc67b95d38378526f&quot;)]</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"5606,-341 0,-341 0,-305 5606,-305 5606,-341\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-319.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [when([(col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;).is_not_nan()) | (col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;).is_null())]).then(col(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;)).otherwise(0.0).alias(&quot;SuccessRate&quot;), when([(col(&quot;Positives&quot;).min()) == (col(&quot;Positives&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;Positives&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_Positives&quot;), when([(col(&quot;ResponseCount&quot;).min()) == (col(&quot;ResponseCount&quot;).max())]).then(col(&quot;SnapshotTime&quot;).max()).otherwise(col(&quot;SnapshotTime&quot;).filter([(col(&quot;ResponseCount&quot;).diff()) != (0.0)]).max()).over([col(&quot;ModelID&quot;)]).alias(&quot;Last_ResponseCount&quot;), [(col(&quot;Positives&quot;)) / (col(&quot;ResponseCount&quot;))].alias(&quot;__POLARS_CSER_0x29cf79554d6c569f&quot;)]</text>\n",
        "</g>\n",
        "<!-- p17&#45;&#45;p18 -->\n",
        "<g id=\"edge19\" class=\"edge\">\n",
        "<title>p17--p18</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M3367.09,-385.47C3248.5,-372.3 3085.12,-354.16 2966.61,-341\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M3352.68,-385.47C3234.93,-372.3 3072.7,-354.16 2955.03,-341\"/>\n",
        "</g>\n",
        "<!-- p19 -->\n",
        "<g id=\"node20\" class=\"node\">\n",
        "<title>p19</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"5218.5,-269 408.5,-269 408.5,-233 5218.5,-233 5218.5,-269\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-247.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical))]</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"5208,-269 398,-269 398,-233 5208,-233 5208,-269\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-247.3\" font-family=\"Times,serif\" font-size=\"14.00\">WITH COLUMNS [col(&quot;Direction&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Channel&quot;).strict_cast(Categorical(None, Physical)), col(&quot;ResponseCount&quot;).strict_cast(Float32), col(&quot;Configuration&quot;).strict_cast(Categorical(None, Physical)), when([(col(&quot;SnapshotTime&quot;).str.slice([dyn int: 4, dyn int: 1])) == (String(-))]).then(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.cast_time_unit()).otherwise(col(&quot;SnapshotTime&quot;).str.strptime([String(raise)]).dt.replace_time_zone([String(raise)]).dt.cast_time_unit()).alias(&quot;SnapshotTime&quot;), col(&quot;Positives&quot;).strict_cast(Float32), col(&quot;Group&quot;).strict_cast(Categorical(None, Physical)), col(&quot;Issue&quot;).strict_cast(Categorical(None, Physical))]</text>\n",
        "</g>\n",
        "<!-- p18&#45;&#45;p19 -->\n",
        "<g id=\"edge20\" class=\"edge\">\n",
        "<title>p18--p19</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M2813.5,-304.7C2813.5,-293.85 2813.5,-279.92 2813.5,-269.1\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2803,-304.7C2803,-293.85 2803,-279.92 2803,-269.1\"/>\n",
        "</g>\n",
        "<!-- p20 -->\n",
        "<g id=\"node21\" class=\"node\">\n",
        "<title>p20</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"2849,-197 2778,-197 2778,-161 2849,-161 2849,-197\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-175.3\" font-family=\"Times,serif\" font-size=\"14.00\">π 12/12</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"2838.5,-197 2767.5,-197 2767.5,-161 2838.5,-161 2838.5,-197\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-175.3\" font-family=\"Times,serif\" font-size=\"14.00\">π 12/12</text>\n",
        "</g>\n",
        "<!-- p19&#45;&#45;p20 -->\n",
        "<g id=\"edge21\" class=\"edge\">\n",
        "<title>p19--p20</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M2813.5,-232.7C2813.5,-221.85 2813.5,-207.92 2813.5,-197.1\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2803,-232.7C2803,-221.85 2803,-207.92 2803,-197.1\"/>\n",
        "</g>\n",
        "<!-- p21 -->\n",
        "<g id=\"node22\" class=\"node\">\n",
        "<title>p21</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"2855.5,-125 2771.5,-125 2771.5,-89 2855.5,-89 2855.5,-125\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-103.3\" font-family=\"Times,serif\" font-size=\"14.00\">RENAME</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"2845,-125 2761,-125 2761,-89 2845,-89 2845,-125\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-103.3\" font-family=\"Times,serif\" font-size=\"14.00\">RENAME</text>\n",
        "</g>\n",
        "<!-- p20&#45;&#45;p21 -->\n",
        "<g id=\"edge22\" class=\"edge\">\n",
        "<title>p20--p21</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M2813.5,-160.7C2813.5,-149.85 2813.5,-135.92 2813.5,-125.1\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2803,-160.7C2803,-149.85 2803,-135.92 2803,-125.1\"/>\n",
        "</g>\n",
        "<!-- p22 -->\n",
        "<g id=\"node23\" class=\"node\">\n",
        "<title>p22</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"2969,-53 2658,-53 2658,0 2969,0 2969,-53\"/>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">TABLE</text>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">π 12/27;</text>\n",
-       "<text text-anchor=\"middle\" x=\"2813.5\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">σ col(&quot;Name&quot;).str.contains([String(Visa)])</text>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"2958.5,-53 2647.5,-53 2647.5,0 2958.5,0 2958.5,-53\"/>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">TABLE</text>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">π 12/27;</text>\n",
+       "<text text-anchor=\"middle\" x=\"2803\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">σ col(&quot;Name&quot;).str.contains([String(Visa)])</text>\n",
        "</g>\n",
        "<!-- p21&#45;&#45;p22 -->\n",
        "<g id=\"edge23\" class=\"edge\">\n",
        "<title>p21--p22</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M2813.5,-88.97C2813.5,-78.51 2813.5,-64.9 2813.5,-53.16\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2803,-88.97C2803,-78.51 2803,-64.9 2803,-53.16\"/>\n",
        "</g>\n",
        "</g>\n",
        "</svg>"
@@ -1675,10 +1685,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:15.008583Z",
-     "iopub.status.busy": "2024-06-27T08:58:15.008324Z",
-     "iopub.status.idle": "2024-06-27T08:58:16.403014Z",
-     "shell.execute_reply": "2024-06-27T08:58:16.402384Z"
+     "iopub.execute_input": "2024-06-28T08:59:32.127523Z",
+     "iopub.status.busy": "2024-06-28T08:59:32.127357Z",
+     "iopub.status.idle": "2024-06-28T08:59:32.916477Z",
+     "shell.execute_reply": "2024-06-28T08:59:32.915963Z"
     }
    },
    "outputs": [],
@@ -1696,10 +1706,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:16.405491Z",
-     "iopub.status.busy": "2024-06-27T08:58:16.405277Z",
-     "iopub.status.idle": "2024-06-27T08:58:27.971055Z",
-     "shell.execute_reply": "2024-06-27T08:58:27.970482Z"
+     "iopub.execute_input": "2024-06-28T08:59:32.919147Z",
+     "iopub.status.busy": "2024-06-28T08:59:32.918704Z",
+     "iopub.status.idle": "2024-06-28T08:59:44.523286Z",
+     "shell.execute_reply": "2024-06-28T08:59:44.522680Z"
     }
    },
    "outputs": [
@@ -1707,7 +1717,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "14.3 ms ± 124 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "14.3 ms ± 63.9 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -1720,10 +1730,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:27.973217Z",
-     "iopub.status.busy": "2024-06-27T08:58:27.972882Z",
-     "iopub.status.idle": "2024-06-27T08:58:34.615445Z",
-     "shell.execute_reply": "2024-06-27T08:58:34.614912Z"
+     "iopub.execute_input": "2024-06-28T08:59:44.525690Z",
+     "iopub.status.busy": "2024-06-28T08:59:44.525267Z",
+     "iopub.status.idle": "2024-06-28T08:59:51.429933Z",
+     "shell.execute_reply": "2024-06-28T08:59:51.429351Z"
     }
    },
    "outputs": [
@@ -1731,7 +1741,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8.19 ms ± 41.8 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "8.51 ms ± 57.6 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -1744,10 +1754,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:34.617736Z",
-     "iopub.status.busy": "2024-06-27T08:58:34.617314Z",
-     "iopub.status.idle": "2024-06-27T08:58:34.625173Z",
-     "shell.execute_reply": "2024-06-27T08:58:34.624750Z"
+     "iopub.execute_input": "2024-06-28T08:59:51.432277Z",
+     "iopub.status.busy": "2024-06-28T08:59:51.431846Z",
+     "iopub.status.idle": "2024-06-28T08:59:51.439385Z",
+     "shell.execute_reply": "2024-06-28T08:59:51.438955Z"
     }
    },
    "outputs": [
@@ -1783,10 +1793,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:34.627120Z",
-     "iopub.status.busy": "2024-06-27T08:58:34.626807Z",
-     "iopub.status.idle": "2024-06-27T08:58:40.710518Z",
-     "shell.execute_reply": "2024-06-27T08:58:40.709902Z"
+     "iopub.execute_input": "2024-06-28T08:59:51.441420Z",
+     "iopub.status.busy": "2024-06-28T08:59:51.441133Z",
+     "iopub.status.idle": "2024-06-28T08:59:51.855262Z",
+     "shell.execute_reply": "2024-06-28T08:59:51.854670Z"
     }
    },
    "outputs": [
@@ -1867,10 +1877,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:40.712810Z",
-     "iopub.status.busy": "2024-06-27T08:58:40.712413Z",
-     "iopub.status.idle": "2024-06-27T08:58:43.372158Z",
-     "shell.execute_reply": "2024-06-27T08:58:43.371679Z"
+     "iopub.execute_input": "2024-06-28T08:59:51.857421Z",
+     "iopub.status.busy": "2024-06-28T08:59:51.857101Z",
+     "iopub.status.idle": "2024-06-28T08:59:52.330575Z",
+     "shell.execute_reply": "2024-06-28T08:59:52.330118Z"
     }
    },
    "outputs": [
@@ -1901,9 +1911,9 @@
     {
      "data": {
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@@ -1953,19 +1963,19 @@
    "execution_count": 16,
    "metadata": {
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-     "iopub.status.busy": "2024-06-27T08:58:43.374063Z",
-     "iopub.status.idle": "2024-06-27T08:58:43.402045Z",
-     "shell.execute_reply": "2024-06-27T08:58:43.401598Z"
+     "iopub.execute_input": "2024-06-28T08:59:52.332708Z",
+     "iopub.status.busy": "2024-06-28T08:59:52.332435Z",
+     "iopub.status.idle": "2024-06-28T08:59:52.359959Z",
+     "shell.execute_reply": "2024-06-28T08:59:52.359491Z"
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@@ -2013,19 +2023,19 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:43.404113Z",
-     "iopub.status.busy": "2024-06-27T08:58:43.403782Z",
-     "iopub.status.idle": "2024-06-27T08:58:43.431558Z",
-     "shell.execute_reply": "2024-06-27T08:58:43.431125Z"
+     "iopub.execute_input": "2024-06-28T08:59:52.362025Z",
+     "iopub.status.busy": "2024-06-28T08:59:52.361630Z",
+     "iopub.status.idle": "2024-06-28T08:59:52.389127Z",
+     "shell.execute_reply": "2024-06-28T08:59:52.388553Z"
     }
    },
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        "                            \n",
-       "var gd = document.getElementById('62f13dc5-94ca-4093-8257-abdbf3288980');\n",
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-       "var gd = document.getElementById('82881bf5-ad9f-48c1-a0ed-526d025eba72');\n",
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@@ -2136,19 +2146,19 @@
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diff --git a/Python/articles/thompsonsampling.html b/Python/articles/thompsonsampling.html
index 7a8bdae3..95f93896 100644
--- a/Python/articles/thompsonsampling.html
+++ b/Python/articles/thompsonsampling.html
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@@ -473,9 +473,9 @@ <h2>Convergence of the Thompson Sampled propensities<a class="headerlink" href="
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diff --git a/Python/articles/thompsonsampling.ipynb b/Python/articles/thompsonsampling.ipynb
index b0280d42..f0d3b43d 100644
--- a/Python/articles/thompsonsampling.ipynb
+++ b/Python/articles/thompsonsampling.ipynb
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@@ -36,10 +36,10 @@
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       "\n",
       "The argument `series` for `cut` is deprecated. It has been renamed to `as_series`.\n",
       "\n",
-      "/tmp/ipykernel_4320/799725524.py:47: DeprecationWarning:\n",
+      "/tmp/ipykernel_4233/799725524.py:47: DeprecationWarning:\n",
       "\n",
       "The `as_series` parameter for `Series.cut` will be removed. The same behavior can be achieved by setting `include_breaks=True`, unnesting the resulting struct Series, and adding the result to the original Series.\n",
       "\n",
-      "/tmp/ipykernel_4320/799725524.py:49: DeprecationWarning:\n",
+      "/tmp/ipykernel_4233/799725524.py:49: DeprecationWarning:\n",
       "\n",
       "`pl.count()` is deprecated. Please use `pl.len()` instead.\n",
       "\n",
-      "/tmp/ipykernel_4320/799725524.py:61: MapWithoutReturnDtypeWarning:\n",
+      "/tmp/ipykernel_4233/799725524.py:61: MapWithoutReturnDtypeWarning:\n",
       "\n",
       "Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.\n",
       "\n"
@@ -74,7 +74,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_4320/799725524.py:66: DeprecationWarning:\n",
+      "/tmp/ipykernel_4233/799725524.py:66: DeprecationWarning:\n",
       "\n",
       "`group_by` iteration will change to always return group identifiers as tuples. Pass `by` as a list to silence this warning, e.g. `group_by(['p'])`.\n",
       "\n"
@@ -84,11 +84,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_4320/799725524.py:162: DeprecationWarning:\n",
+      "/tmp/ipykernel_4233/799725524.py:162: DeprecationWarning:\n",
       "\n",
       "`where` is deprecated. Use `filter` instead.\n",
       "\n",
-      "/tmp/ipykernel_4320/799725524.py:162: DeprecationWarning:\n",
+      "/tmp/ipykernel_4233/799725524.py:162: DeprecationWarning:\n",
       "\n",
       "`where` is deprecated. Use `filter` instead.\n",
       "\n"
@@ -356,10 +356,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:54.481595Z",
-     "iopub.status.busy": "2024-06-27T08:58:54.481177Z",
-     "iopub.status.idle": "2024-06-27T08:58:54.504530Z",
-     "shell.execute_reply": "2024-06-27T08:58:54.504005Z"
+     "iopub.execute_input": "2024-06-28T09:00:03.645403Z",
+     "iopub.status.busy": "2024-06-28T09:00:03.644993Z",
+     "iopub.status.idle": "2024-06-28T09:00:03.668396Z",
+     "shell.execute_reply": "2024-06-28T09:00:03.667769Z"
     }
    },
    "outputs": [
@@ -390,9 +390,9 @@
     {
      "data": {
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                       {\"template\":{\"data\":{\"scatter\":[{\"type\":\"scatter\"}]}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Number of positive responses in the Adaptive Model\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Sampled Propensity\"},\"range\":[0,0.2],\"tickformat\":\",.0%\",\"tickmode\":\"array\",\"tickvals\":[0,0.01,0.05,0.1]},\"coloraxis\":{\"colorbar\":{\"title\":{\"text\":\"Propensity\"}},\"colorscale\":[[0.0,\"#440154\"],[0.1111111111111111,\"#482878\"],[0.2222222222222222,\"#3e4989\"],[0.3333333333333333,\"#31688e\"],[0.4444444444444444,\"#26828e\"],[0.5555555555555556,\"#1f9e89\"],[0.6666666666666666,\"#35b779\"],[0.7777777777777778,\"#6ece58\"],[0.8888888888888888,\"#b5de2b\"],[1.0,\"#fde725\"]],\"showscale\":false},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Thompson Sampling\"}},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('75de5a00-28de-4235-9ce3-71d1e847d5c5');\n",
+       "var gd = document.getElementById('6ea0ed28-b3b5-4c1c-8975-808c7efdafec');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -450,19 +450,19 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:54.507862Z",
-     "iopub.status.busy": "2024-06-27T08:58:54.507470Z",
-     "iopub.status.idle": "2024-06-27T08:58:54.511785Z",
-     "shell.execute_reply": "2024-06-27T08:58:54.511204Z"
+     "iopub.execute_input": "2024-06-28T09:00:03.671853Z",
+     "iopub.status.busy": "2024-06-28T09:00:03.671411Z",
+     "iopub.status.idle": "2024-06-28T09:00:03.675708Z",
+     "shell.execute_reply": "2024-06-28T09:00:03.675274Z"
     }
    },
    "outputs": [
     {
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@@ -524,19 +524,19 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-27T08:58:54.513803Z",
-     "iopub.status.busy": "2024-06-27T08:58:54.513467Z",
-     "iopub.status.idle": "2024-06-27T08:58:54.521135Z",
-     "shell.execute_reply": "2024-06-27T08:58:54.520571Z"
+     "iopub.execute_input": "2024-06-28T09:00:03.677836Z",
+     "iopub.status.busy": "2024-06-28T09:00:03.677428Z",
+     "iopub.status.idle": "2024-06-28T09:00:03.685251Z",
+     "shell.execute_reply": "2024-06-28T09:00:03.684800Z"
     }
    },
    "outputs": [
     {
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        "                            \n",
-       "var gd = document.getElementById('4d25cfeb-44c6-40d0-966f-45eac5474e03');\n",
+       "var gd = document.getElementById('1c9107df-0410-454f-81ed-f343085d07c3');\n",
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@@ -727,19 +727,19 @@
    "execution_count": 8,
    "metadata": {
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-     "iopub.execute_input": "2024-06-27T08:58:54.535435Z",
-     "iopub.status.busy": "2024-06-27T08:58:54.535106Z",
-     "iopub.status.idle": "2024-06-27T08:58:54.539476Z",
-     "shell.execute_reply": "2024-06-27T08:58:54.539045Z"
+     "iopub.execute_input": "2024-06-28T09:00:03.699140Z",
+     "iopub.status.busy": "2024-06-28T09:00:03.698817Z",
+     "iopub.status.idle": "2024-06-28T09:00:03.703124Z",
+     "shell.execute_reply": "2024-06-28T09:00:03.702690Z"
     }
    },
    "outputs": [
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index 764c901f..a93ee4a3 100644
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