diff --git a/Chapter 7 - Unsup. Learning - Dimensionality Reduction/CaseStudy3 -Bitcoin Trading - Enhancing Speed and accuracy/BitcoinTradingEnhancingSpeedAccuracy.ipynb b/Chapter 7 - Unsup. Learning - Dimensionality Reduction/CaseStudy3 -Bitcoin Trading - Enhancing Speed and accuracy/BitcoinTradingEnhancingSpeedAccuracy.ipynb
index e8802b9..2be1b64 100644
--- a/Chapter 7 - Unsup. Learning - Dimensionality Reduction/CaseStudy3 -Bitcoin Trading - Enhancing Speed and accuracy/BitcoinTradingEnhancingSpeedAccuracy.ipynb	
+++ b/Chapter 7 - Unsup. Learning - Dimensionality Reduction/CaseStudy3 -Bitcoin Trading - Enhancing Speed and accuracy/BitcoinTradingEnhancingSpeedAccuracy.ipynb	
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 1,
    "metadata": {
     "_cell_guid": "5d8fee34-f454-2642-8b06-ed719f0317e1"
    },
@@ -169,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "_cell_guid": "52f85dc2-0f91-3c50-400e-ddc38bea966b"
    },
@@ -180,7 +180,7 @@
        "(2841377, 8)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -192,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -302,7 +302,7 @@
        "2841376     2205.648801  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -315,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "_cell_guid": "7bffeec0-5bbc-fffb-18f2-3da56b862ca3"
    },
@@ -466,7 +466,7 @@
        "max         2.754e+03  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -495,7 +495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -520,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -529,7 +529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -555,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -729,7 +729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -797,7 +797,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -995,7 +995,7 @@
        "[5 rows x 29 columns]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1242,7 +1242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -1266,16 +1266,16 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Open</th>\n",
-       "      <th>High</th>\n",
-       "      <th>Low</th>\n",
        "      <th>Close</th>\n",
        "      <th>Volume_(BTC)</th>\n",
-       "      <th>Volume_(Currency)</th>\n",
        "      <th>Weighted_Price</th>\n",
-       "      <th>short_mavg</th>\n",
-       "      <th>long_mavg</th>\n",
        "      <th>signal</th>\n",
+       "      <th>EMA10</th>\n",
+       "      <th>EMA30</th>\n",
+       "      <th>EMA200</th>\n",
+       "      <th>ROC10</th>\n",
+       "      <th>ROC30</th>\n",
+       "      <th>MOM10</th>\n",
        "      <th>...</th>\n",
        "      <th>RSI200</th>\n",
        "      <th>%K10</th>\n",
@@ -1292,16 +1292,16 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2841372</th>\n",
-       "      <td>2190.49</td>\n",
-       "      <td>2190.49</td>\n",
-       "      <td>2181.37</td>\n",
        "      <td>2181.37</td>\n",
        "      <td>1.700</td>\n",
-       "      <td>3723.785</td>\n",
        "      <td>2190.247</td>\n",
-       "      <td>2179.259</td>\n",
-       "      <td>2189.616</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>2181.181</td>\n",
+       "      <td>2182.376</td>\n",
+       "      <td>2211.244</td>\n",
+       "      <td>0.431</td>\n",
+       "      <td>-0.649</td>\n",
+       "      <td>8.42</td>\n",
        "      <td>...</td>\n",
        "      <td>46.613</td>\n",
        "      <td>56.447</td>\n",
@@ -1316,16 +1316,16 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2841373</th>\n",
-       "      <td>2190.50</td>\n",
-       "      <td>2197.52</td>\n",
-       "      <td>2186.17</td>\n",
        "      <td>2195.63</td>\n",
        "      <td>6.561</td>\n",
-       "      <td>14402.812</td>\n",
        "      <td>2195.206</td>\n",
-       "      <td>2181.622</td>\n",
-       "      <td>2189.877</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>2183.808</td>\n",
+       "      <td>2183.231</td>\n",
+       "      <td>2211.088</td>\n",
+       "      <td>1.088</td>\n",
+       "      <td>-0.062</td>\n",
+       "      <td>23.63</td>\n",
        "      <td>...</td>\n",
        "      <td>47.638</td>\n",
        "      <td>93.687</td>\n",
@@ -1340,16 +1340,16 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2841374</th>\n",
-       "      <td>2195.62</td>\n",
-       "      <td>2197.52</td>\n",
-       "      <td>2191.52</td>\n",
        "      <td>2191.83</td>\n",
        "      <td>15.663</td>\n",
-       "      <td>34361.024</td>\n",
        "      <td>2193.792</td>\n",
-       "      <td>2183.605</td>\n",
-       "      <td>2189.943</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>2185.266</td>\n",
+       "      <td>2183.786</td>\n",
+       "      <td>2210.897</td>\n",
+       "      <td>1.035</td>\n",
+       "      <td>-0.235</td>\n",
+       "      <td>19.83</td>\n",
        "      <td>...</td>\n",
        "      <td>47.395</td>\n",
        "      <td>80.995</td>\n",
@@ -1364,16 +1364,16 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2841375</th>\n",
-       "      <td>2195.82</td>\n",
-       "      <td>2216.00</td>\n",
-       "      <td>2195.82</td>\n",
        "      <td>2203.51</td>\n",
        "      <td>27.090</td>\n",
-       "      <td>59913.493</td>\n",
        "      <td>2211.621</td>\n",
-       "      <td>2187.018</td>\n",
-       "      <td>2190.204</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>2188.583</td>\n",
+       "      <td>2185.058</td>\n",
+       "      <td>2210.823</td>\n",
+       "      <td>1.479</td>\n",
+       "      <td>0.297</td>\n",
+       "      <td>34.13</td>\n",
        "      <td>...</td>\n",
        "      <td>48.213</td>\n",
        "      <td>74.205</td>\n",
@@ -1388,16 +1388,16 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2841376</th>\n",
-       "      <td>2201.70</td>\n",
-       "      <td>2209.81</td>\n",
-       "      <td>2196.98</td>\n",
        "      <td>2208.33</td>\n",
        "      <td>9.962</td>\n",
-       "      <td>21972.309</td>\n",
        "      <td>2205.649</td>\n",
-       "      <td>2190.712</td>\n",
-       "      <td>2190.510</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>2192.174</td>\n",
+       "      <td>2186.560</td>\n",
+       "      <td>2210.798</td>\n",
+       "      <td>1.626</td>\n",
+       "      <td>0.516</td>\n",
+       "      <td>36.94</td>\n",
        "      <td>...</td>\n",
        "      <td>48.545</td>\n",
        "      <td>82.810</td>\n",
@@ -1412,35 +1412,35 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 29 columns</p>\n",
+       "<p>5 rows × 23 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "            Open     High      Low    Close  Volume_(BTC)  Volume_(Currency)  Weighted_Price  \\\n",
-       "2841372  2190.49  2190.49  2181.37  2181.37         1.700           3723.785        2190.247   \n",
-       "2841373  2190.50  2197.52  2186.17  2195.63         6.561          14402.812        2195.206   \n",
-       "2841374  2195.62  2197.52  2191.52  2191.83        15.663          34361.024        2193.792   \n",
-       "2841375  2195.82  2216.00  2195.82  2203.51        27.090          59913.493        2211.621   \n",
-       "2841376  2201.70  2209.81  2196.98  2208.33         9.962          21972.309        2205.649   \n",
+       "           Close  Volume_(BTC)  Weighted_Price  signal     EMA10     EMA30    EMA200  ROC10  \\\n",
+       "2841372  2181.37         1.700        2190.247     0.0  2181.181  2182.376  2211.244  0.431   \n",
+       "2841373  2195.63         6.561        2195.206     0.0  2183.808  2183.231  2211.088  1.088   \n",
+       "2841374  2191.83        15.663        2193.792     0.0  2185.266  2183.786  2210.897  1.035   \n",
+       "2841375  2203.51        27.090        2211.621     0.0  2188.583  2185.058  2210.823  1.479   \n",
+       "2841376  2208.33         9.962        2205.649     1.0  2192.174  2186.560  2210.798  1.626   \n",
        "\n",
-       "         short_mavg  long_mavg  signal  ...  RSI200    %K10    %D10    %K30    %D30   %K200  \\\n",
-       "2841372    2179.259   2189.616     0.0  ...  46.613  56.447  73.774  47.883  59.889  16.012   \n",
-       "2841373    2181.622   2189.877     0.0  ...  47.638  93.687  71.712  93.805  65.119  26.697   \n",
-       "2841374    2183.605   2189.943     0.0  ...  47.395  80.995  77.043  81.350  74.346  23.850   \n",
-       "2841375    2187.018   2190.204     0.0  ...  48.213  74.205  82.963  74.505  83.220  32.602   \n",
-       "2841376    2190.712   2190.510     1.0  ...  48.545  82.810  79.337  84.344  80.066  36.440   \n",
+       "         ROC30  MOM10  ...  RSI200    %K10    %D10    %K30    %D30   %K200   %D200      MA21  \\\n",
+       "2841372 -0.649   8.42  ...  46.613  56.447  73.774  47.883  59.889  16.012  18.930  2179.259   \n",
+       "2841373 -0.062  23.63  ...  47.638  93.687  71.712  93.805  65.119  26.697  20.096  2181.622   \n",
+       "2841374 -0.235  19.83  ...  47.395  80.995  77.043  81.350  74.346  23.850  22.186  2183.605   \n",
+       "2841375  0.297  34.13  ...  48.213  74.205  82.963  74.505  83.220  32.602  27.716  2187.018   \n",
+       "2841376  0.516  36.94  ...  48.545  82.810  79.337  84.344  80.066  36.440  30.964  2190.712   \n",
        "\n",
-       "          %D200      MA21      MA63     MA252  \n",
-       "2841372  18.930  2179.259  2182.291  2220.727  \n",
-       "2841373  20.096  2181.622  2182.292  2220.295  \n",
-       "2841374  22.186  2183.605  2182.120  2219.802  \n",
-       "2841375  27.716  2187.018  2182.337  2219.396  \n",
-       "2841376  30.964  2190.712  2182.715  2218.980  \n",
+       "             MA63     MA252  \n",
+       "2841372  2182.291  2220.727  \n",
+       "2841373  2182.292  2220.295  \n",
+       "2841374  2182.120  2219.802  \n",
+       "2841375  2182.337  2219.396  \n",
+       "2841376  2182.715  2218.980  \n",
        "\n",
-       "[5 rows x 29 columns]"
+       "[5 rows x 23 columns]"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1459,17 +1459,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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BScum4azBoEBE5AFOXbTNFMcMCkREHuD6TT4pEBHRbzJ+bfnANYBBgYiI6mBQICIiCYMCERFJGBSIiEjCoEBERBIGBSIikjAoEBF5mNih7Zu9L4MCEZGH6dbJt9n7MigQEbk5IYwn2BnVP7DZx2JQICJyc8d/Mc57JJPJmn0sBgUiIjd3Jrs2xUX9yXaaikGBiMjN1W092vBEjxYdi0GBiMjN1W0t8pXzSYGIqFX76ke1zY5ldVBQq9UYN24ccnNzAQBLly5FVFQUxo8fj/Hjx+Ozzz4DAKSnpyMuLg5RUVHYsGGDtP+5c+cQHx+P6OhoLFu2DDqdbXJ/ExG1dnJv2x3LqqBw+vRpTJ48GdnZ2dKys2fPYvv27dizZw/27NmD0aNHo7KyEklJSdiyZQv279+Ps2fP4siRIwCARYsW4ZlnnsHBgwchhMCuXbtsVwsiolasU5DcZseyKijs2rULK1euhFKpBABUVFQgLy8PSUlJiIuLw8aNG2EwGHDmzBmEh4cjLCwMcrkccXFxSEtLw5UrV1BZWYlBgwYBAOLj45GWlmazShARtWZy7+Z3QTU5ljUbrVmzxuhzYWEhRowYgZUrVyIoKAiJiYn46KOPEBAQAIVCIW2nVCqRn5+PgoICo+UKhQL5+fk2qgIRUet2vdR2zfHNeuYICwvD5s2bpc/Tpk1DamoqoqOjjQZNCCEgk8lgMBjMLm+KTp2aP0LP3SgUQc4ugsOwrp6JdXWsSm1tn9SWlqdZQeH8+fPIzs5GdHQ0gOqLvFwuR0hICFQqlbSdSqWCUqk0WV5YWCg1RVnr+nU1DAbR+IZuTqEIgkp109nFcAjW1TOxrs4T4OcllcfLS9asm+lmdUkVQuD5559HSUkJtFotdu7cidGjR2PgwIHIyspCTk4O9Ho99u3bh4iICISGhsLPzw8ZGRkAgD179iAiIqI5X01ERBa0C2h5N6RmPSn069cPM2fOxOTJk6HT6RAVFYVx48YBAJKTkzFv3jxoNBpERkYiJiYGAJCSkoLly5dDrVZjwIABSEhIaHHhiYio1m3d27T4GDJRP72ei2LzkedhXT0T6+p4CeszAQCvzumJAL/qBiCHNh8REZHrqQkILcGgQEREEgYFIiKSMCgQEZGEQYGIiCQMCkRELkyrE1BX6C2uv3HTthmnGRSIiFzYuv9cxeytORbXP/XGJZt+H4MCEZELO3+l0qHfx6BAREQSBgUiIhdVM1LZGn+f0NUm38mgQETkAQb08LfJcRgUiIjcwMa91xzyPQwKRERu4OSv5SbLtn2uMrNlyzAoEBG5oZ8vV+CLM7bP0Nqs+RSIiMi+dhy+bnFd+jk1Xj1QYJfv5ZMCEZGL0ekFDn5XYnF9/YDw0Ihgm303gwIRkYvRN3FCsVu726bnEcCgQETkcvQG88stTZRpq+6oAN8pkBMdPl0EudDh1tCWzytL5EnKNeajgkYrUFllOTmeLTAokNO88EF1Iq9lE7sxMBDVkVOgMbv8lyuVSNlt3/EKbD4ip1uzM8/ZRSByqC2f5iNhfabF5qCjP5rvaqoz867hlcQeNi0bnxSIiBzsm/NlAICySgMC/b1N1mdcNB2oBgC5hVVGn99d2NvmZeOTAhGRk1jTx8hLVvvnj74ukv7s4y0zs3XLMSgQEbkwCy1M2DI73C7fx6BAROQk9S/4lVUGFJbWTq/5SmIPi08Tfj72uXzznQIRkYuYuSnb6HP7to6/RPNJgYjISQyW2oacyKqgoFarMW7cOOTm5gIA0tPTERcXh6ioKGzYsEHa7ty5c4iPj0d0dDSWLVsGna76MSgvLw9Tp05FTEwMZs2ahbKyMjtUhdzJD9nme1cQtSp1YsLPuRXOK0cdjQaF06dPY/LkycjOzgYAVFZWIikpCVu2bMH+/ftx9uxZHDlyBACwaNEiPPPMMzh48CCEENi1axcAYNWqVZgyZQrS0tJw++23Y8uWLfarEbmFSq3r3SEROVp5Ve3I5ed3XXViSWo1GhR27dqFlStXQqlUAgDOnDmD8PBwhIWFQS6XIy4uDmlpabhy5QoqKysxaNAgAEB8fDzS0tKg1Wpx4sQJREdHGy2n1q2tH1suic7nVlpcNydWaXGdv699uqMCVrxoXrNmjdHngoICKBQK6bNSqUR+fr7JcoVCgfz8fBQVFSEwMBByudxoObVu9bNACiEgk1X/RVeVaJF5TYPhtwY6o2hEdlVRJ6+Rt5fli/vQvm0trnthephNy1RXk19tGwwG6R8vUPuP2dLyuv/Ya9T/bI1OnVrPBUKhCHJ2Eewu8LpxUPj0uzI8FtMVADBryw8oqzRg3Kiuziia3bSG81qDdbXs+M+l0p+zVDr80cL+IV3aAQAm/kGJnYeN50/o26tDE0tpvSYHhZCQEKhUtfOCqlQqKJVKk+WFhYVQKpXo2LEjbt68Cb1eD29vb2n7prp+XQ1DE3OMuyOFIggqle2n2HM1VZXGCb92HSnAuCHVgb+ssvpOypN+h9ZyXgHWtTEr38mS/vzfU0V49D7zF/ia48beFQjoddj51Q2TdQ3x8pI162a6yQ27AwcORFZWFnJycqDX67Fv3z5EREQgNDQUfn5+yMjIAADs2bMHERER8PHxwdChQ7F//34AQGpqKiIiIppcUPIsX54tbXwjolZq/HDjmdQU7R03XqHJ3+Tn54fk5GTMmzcPGo0GkZGRiImJAQCkpKRg+fLlUKvVGDBgABISEgAAK1euxJIlS7B161Z07doV69evt20tyO1odY0/9en0AnI75XchchWl5XoUl+mMlg37nfH7hLZtTJPm2YvVQeGLL76Q/jxy5Ejs3bvXZJt+/frho48+MlkeGhqK9957r5lFJE8UrvSTMkVa8nNuBW4PD3BQiYicY+6rOSbLQoJ9jD73D2uDcKUvcgqqTLa1NfYLJKc41MCk5DUyfuUAN/IsCeszG91mdqwSvvXyGslkMtx3Rzt7FcsIgwI5RVFZ41MK/vc03ztQ6zL9gc4YYaEr9pBbHPPUzKBALsPSLFRErcX9d1p+Gqh5v3b37yyPX7AFZkkll1FYqoOivU/jGxJ5oPvuaHi8Q9s23lgzrTu6dLDvZZtPCuQy/vbWZZNlrWFsCrU+d/b0N/o84ta2eGy0wsLWtcIUvvCV2/eyzaBALm36S1km89ISubsHBrU3+uxKvewYFMjhmppDPundXCx7NxeqEq2dSkTkWPJ6ww7s/Z6gKRgUyOEMBsvrLL1svlxYZbZ5icgdedXL/+bn4zqDNBkUyOFKyi13R9U10lP1xk1dwxsQuaiazMD39AtE986+RuuakyTUXhgUyOF0DaS4+PWq5fzyAPD6QVWD64lc0S9XKvHYS9WJ8NJ/VqNdgOPSVjQVu6SSw7VpYIKQjF8bTn3x0yXXmLKQ3FPNiOJ3FvRyyN15YakOC9+8ZLRscmRHu39vSzAokMPpG3incOgURzGT/WUXVKFXFz+7Hb+yyoCzORVm5yIfMyTYzB6ug0GBHG7th3lWbff2k71QrjGYTRhG1BJnc8ptHhQMBoF9J4oxsFcAVmy/YrJeBuCdhb2lz8r2chSU6EzSZDsbgwI5XH5x9cticzNK1birTwDk3jKXbnsl93XilzLE3W3b2cv+uiUblVUCH31dZHZ93YAAAOtmhOHEL2Uu1R0V4ItmcqKMXyzPHvXYA50trmOOJGqOb8+rpT83NDdyc1VWWf57+W69gABUd0sdfmugS/U8AhgUyIl0esv/iNq3rX2IffvJXkYv5xa+WT1eIWF9Jl49YP5Jg6i+LZ/W/l25eE2DXV/dwHUHdHF+0MWahxrDoEBO0z7QutZLubcMt3WvzRVT9x9y+jm1uV0aZRCCeZVamfpne9+JYrx2oABVOgPKKhtP5d4Qc/vLZNVPCBN+79q9jepjUCCn+SHT/AV9+K1m2lht/IQ9fUMWpr+UxdQZrdzPuZV4fGM2Zm3JQUlZ858aZm0x7QzxSmJ4S4rmNAwK5DRxI82/N/iLuWyR9W7z6jY9FRQ3/8JeN3VG5jUNPk6/0exjkXub99olzNma3eLjxN/TAS9M7+62nSQYFMhp/P3M//Vr42u6vH5Lz1uf1Y5sfvrtyyhtIHVGY5a9mwsA+Mf7V5D6TTEyfi3jy+xW6maFAdeKrL/JMBiE0YDKN+f3xPjhweja0beBvVwbu6SS05i7+FtmfJH++ifjpqe5r+aY7eFhjcv1UnO/vDf/t/LJ8PrcXs06JrmvknI9QjoYT/ZUoTEg70YVenbxk3ouCSEQu+yM0Xb2nuvAEdy/BuS2mhIUrLlx11v54thc6m5zLworq4RVE62T+3t3YW/88Z7qcQtrdpoOrkzcnI1V/87Dqn9XD0rTaA3Y9ZVxU+PgPq4zJ0JLMCiQ02gbSIxXnzWjT2sSjjXmksp00h5zLwprrPvPVasDDrmfh0ZUdxmtm7n0yvXavyOXVBrpz9n5VUhYn4knXsnGpydLjI6zYHyInUvqGAwK5DQ+cuu7FHlZOdjImncLz5hJQVBX2zbG/yzO5lTgsZey+NTgxg5+V2KybO2j3RHayQf33dkOADCod+2d/tJ3cpGdr0FZpR7L32v478trc3o2u+nSFfGdAjlNkH/L//o9NCIYPbv44aU91e8BWvJuocbW2T0BgEHAg+w4fN1kWWgnX6x9NEz6XH+U8zM7jIPBraFtcP5KbWr3dxb0glLZDiqV5ZH57ohBgZwm0L/lXfZSvynGtqds9zL44ZG1+XD++Zcwk9neSsv1btvVsLVSV7RsYFqNp8Z3Qds2nn/u2XxETtMhyPSeZNXU0CYfp27TUqffjllarkdWvsZk27pdTaf+oZPJ+roZKxXtfUzW/8j5HNxObp3eZf/8S1gDWzasNQQEoIVPCtOmTcONGzcgl1cf5tlnn8WlS5ewdetW6HQ6PProo5g6dSoAID09HWvXroVGo8GYMWOwYMGClpee3FqnIB94exnPr9DUdMZ/GmWcQqCHsvplYU267fpNSVv31+a/ib6rvdSsMC+uC65crzJ5d/HijDCUluvx3Ad50v539vRvNRcIT/D8h1elP5sL9OYkxihQpjHg/wa2s0vyPFfW7KAghEB2djb+97//SUEhPz8fCxYswMcffwxfX19MmjQJw4cPR/fu3ZGUlIT33nsPXbt2RWJiIo4cOYLIyEibVYTcj5cXsOGJHiirNGDpO7mNbj+qfyCO1hufED24+iXh+sfDsPDNyzh10XhSk3KNAQF1Bsl9c954ZrfV00Jx6mI5hvVti2F9TdNrdAn2QZdg4wvJrC05WPhQiNGLSXJNdXu4hSutH1D2+/5B9iiOW2h2UMjMrH4JN2PGDBQXF+ORRx5B27ZtMWLECAQHVz+CR0dHIy0tDXfffTfCw8MRFlb96BYXF4e0tDQGhVbOSyZDcFs5gq1MJz8zRomZMUrkFGikSUx8faov+J3b1V6463Yf/evmbLz9ZC/Ivc3f7fVQ+KGHovGnkwA/L5Rrah9p1qde86geJ7amNwh4yZw/IX2Rujaf0bO/NU2+Ob+nVeNeWqtmv1MoLS3FyJEjsXnzZmzbtg0ffPAB8vLyoFDU5q1RKpXIz89HQUGB2eXUulnbzbS+oEZeUL/48TWjz/8+Ytrz5NU5PZv0nfcOCDRZtmTbZaM+7FStrFKPx17Kwr4Txc4uCvLr5MWqCVC+ci/4+fB1qiXNflIYPHgwBg8eLH2eMGEC1q5di1mzZknLhBCQyWQwGAxGdww1y5uiUyfTf5SeSqFoHY+uMplpXa2qu2/1i8NAf2+z2/9U72XwZ9+XYuFE4x5KYd3aNSkozY0PxMHvjFMa5N3QYvl7V+DnI0Pqs3c2eozWcl4vFVR32/zmfDlmxPZwalmunaltTmzK72+vbd1Bs4PCyZMnodVqMXLkSADVF/rQ0FCoVLWJylQqFZRKJUJCQswub4rr19WtIv+9QhHkcf2e66rb+8fbCyZ1tabuNfMp+Hhbtz0AfP29Cr8LbSO92L5+vXnzMJij0YpGy+Hp57Uuvf63y4ow/V3OZJcj5bcnuXcW9IJMJsP3meW4LaxNi+/ea8aVrJjUDQe/K8GcWCX+81V1x4K2bbya9Ptbu60rn1cvL1mzbqabfRZu3rxspLqRAAATr0lEQVSJdevWQaPRQK1WY/fu3XjxxRdx7Ngx3LhxAxUVFTh06BAiIiIwcOBAZGVlIScnB3q9Hvv27UNERERzv5rcWEWdKQu9mtneXBNXmrL36p15SFifadTTqbkSY0xTe7/3RaHZnEo1Jq3+EX//12WL6z2J9re05uZGl6fUadp7dEMWjv2sxvrUa3jilWyzx1JX6I1STlhSWVV7Yp/7IA/HfynDoxuypPdAEQOsu5tPnt4dLz3h3KcbZ2v2k8J9992H06dP46GHHoLBYMCUKVMwZMgQLFiwAAkJCdBqtZgwYQLuvLP6sTo5ORnz5s2DRqNBZGQkYmJibFYJch/nLtc27TT3nYKUMdVJ7zDv/l0gft8/CG8dUuHI2eq7xM++L0W/7m0w7Hfm78xKynQoKTO7yuPUTFxUYkXKkbpdhDN+LcOQW4x7HczeWpuTatnEbrg1tI3Z48zclN3g94y727opMbu5ccprW2nROIWnnnoKTz31lNGyuLg4xMXFmWw7cuRI7N27tyVfRx6gbroBSz2CGlP7pGC8/7C+bXHiQuNX3mn3mQ5as8b6x8Pwc26llLPpL1EKdAn2wa6j1dky3/niusWg0JqcPF9qdnljc1S8vDcfL0zvLs1FUH/7NTvzMOUPnTCoVwA+/74Eh06Z/x4AePT/OuOd/xZKnxvrnEC1mOaCHKqw1HYTpddvfZo7TolHNxhnSr3vziD870xtm+/6x3ugc7vm/bXv3M4Ho/obj1k4fqH23URNc0lN27a5Lqu/XKnE7yzc7XqKYrX5c/zpCdOkdPX9fVuu9LstNtPc9v7h63jfTB6jGltnh0sDCyNvD0JpuR4dAhkQmoL9ssjt1IwwDW5r/I9dJpNhxaRuRssee0CBdxf2xvrHe+Ddhb2bHRAsGXmr8ZNB3ZQK32eW198cq3fmeXw31tDO5sd91DxRAcA/plSfp9BOPibBM2F9JorVOuQXW38DMeSWALw2t6fRSHO5twwdg+ROHyvhbvikQG6nY5AciTEK3NHTdERx325tIPcGdHog4f7aZiJbB4Ma/cL8jT4nvVs7Mnt96jW8Oa+nNMCuRk0q5vWPhxkNuvMU14pqA+N3F8twV5+2+Pqn2qe1e24LRO+QNnhrfi+L6dPnv35J+vO/nuqF+a/l4GaFAaP6ByK/WItH7u2Evt38mt1ZgSxjUCCnePph4wlJnog27dHTkIbSEFRfKASG32r/9n1FI8Hm16sai3M8LHzzcoOjrd1VYZ0BYy/tycfrc3vitbTaLuk1vbesnU/D20uG5RO7YdvnhZh2f2f4N2kaV2oq/rrkMP/6vPbCcGcv47v8ewcE4V4ruw02pqZTkyPymAX6e2PzrHCL7dbJH13Fljo9bOqb8XKWVRMDuZPzucbNZsl1EtIBTUt98drcngCArh19sfSRbgwIDsAnBXKYui987am6q2vTR803V5C/N16eGW40KU/bNl4oq7RuUMTcV3OQMiMMymDPa0oCgEwzKcwteX1uT+QUaNBD6ccA4CT81cnjJNzfGUH+XvDzcV6zzJZZ4SbLlv6pKwDgrfm98ML07kbrnn77ssVeO55iYC9/i0kE//VUL/zrqV5o4+uFW7v7MyA4EX958jj33BaIzbN6OjUPvkwmwzsLavMtdevki9vCqi+KPnIZunb0NZkxbv7rl6TpHgtLtajS2mD4tZX+e7oUC9+81PiGjSirtNwUNju2i8V13l6yVjdvgatiUCCyE5lMhgm/r57e862nbzNZb25E90e/ddtc+OZlPG4h9cMllQYJ6zNx00bTTALAO/8tRGGpDgaDQMrHV5s9w9ysLTkW1/Hu3z3wnQKRjQT5e+FmhQEPDGonLXtweAc8OLyDxX2eiFZAbxC4s2cAnnrjktHE8OZ8ePQGPjlenZJ6ztYcvDm/J3zltrvYTn+pevDfmezqoFCTtK4lbHEMchyGbiIbqUlx0b/e2IWG3DsgCH+4o51R76X6vZEqqgxSPqGagFDj8Y3ZzSxt7XfVfUFe36Mbsqya+F6nF0bHeWdBL2ydHY7X5/ZkQHAzfFLwYJdUGnyfWd7gnaozDOxl/UXTnUy6tyOU7eW465amT9NZ98L55OvGTTCJvyV7szTpfM3FuHeIH/4xJdSq7zv+ixqdguRY9e+8RredvTWn0bv9+a/VlrmH0g8ymYzzWLspBgUPVjNyNu7uYKffrdVNbrZgfEgDW7qvNr5eGDvUumycDamb3vvVA7VjHP72Vm0uoOkPdMa2zwvr7obMaxoIIVBarkd2fhX+mVqdprpmKtEnohXSWJBN+0zHTtQfYVz3zn/3sSI8PLKDlFvq7SerX5Jv3V9gkoTwtQX9XHaOAWocg0IrYBCAswfNlpTVNkE0P2W2Zxs9qB0++94482f6OdPJgBb/MQT9e/hj/4liFJQYd2OtnxAQgDSnwBsHVXjjoMpkPQBse6qXyXl5d2FvzHstByVleqR+U4zUb2qbrma8bPo9NfuQe2NQaAWK1Hq75f6xVs1saWRZuwDrmltuD69unkr5Sw+cuFCG7zPL0K+7v8ULviXDb22LMUPao3eI5aytd/UOwP9+aPyu39YvvMl5GBRagcby2NuTRmswmlWrdxfzGTQJuKtPW/wnvcjsut/fFojEMaZT2A7r2xbD+lZPTFM/KGyeFY7ANl5S0+HFq5XSOwRrewQ9+kBno6CQMiMMMhlw8aoGeoNoMAcVuScGhVagZnpEZ6g/zeLiCV2dUxA3EORfe6ft7QU8MzkUBoNAry5+VjW5vT63J6p0wuITR5+ubZrcvOMlk+H5hO5IejcXTz7YRUrFoWjvmSk5iEGhVdA5MSjUtXpaKAL82MRgSd1sqW8/2fS+/W18vdDGDrNJdu/sy7EGrQj/hbYC6goDnn7rEn6+3LxRqrbSQ8Gmo4bU7fnjahdgVysP2Q+Dgoc6/kttr5WL1ypRUKLD8x9edegL32/P15ahJt0DWVYTFDoGsX8/OQ+bjzxU3X7oHx6tfXm54I1LUrvyiu25yCmowuSIjhjTjP71VVoDHn8lG3JvYGS/QPjJvRA3PBhPv3UZ/3w8DJs/rS2Dqw2gc0VeMhlem9PTqdldiWTCmV1TmuD6dTUMBrcoaosoFEE2GfjTUOoCS5rSbqwq0RoNpmqIpWkXbVVXd8C6eiZXrquXlwydOjV99kE+KZCkZuDT4D4BWDA+REqD3LaNNwpLdThwshgRtwchK1+Dtz8rbOhQkk1/Dbd62kUicj4GBQ9k7VNCzcTx9bc/dbHc4jHqj7hdPS0UHQPl8PaSwf+3nkU1+3J0K5H7YVDwMC/vudboNltnh8Pf10vq+/70wyFI2d34fnXdEe6Pp+ND2CuFyMMwKHgQIQQyLpabXTczRoFeXfyQX6w1yV55Z68Ao7v6974oNHkimPD7hucFqGvTX02noiQi98Cg4AFyC6uQ9G6u0bK35vfCE69koebd/IAe/ugQKEdop8ZHN027v7MUFLYt6AWvJj4NWJvDh4hcj0ODwieffIKtW7dCp9Ph0UcfxdSpUx359R7nbE451v3HtNmnbzc/+MhlWD2tOj1Bh7be6BDYtFPN9wFErZPDgkJ+fj42bNiAjz/+GL6+vpg0aRKGDx+OW265xVFFsDl1hR4VVQab5IERQkBvqO77rzcIyGSADNW59UvL9Tj2sxp7vy3C728Lwolfy4xSUdf1xrye8POpfuHbvbMvL+5E1CQOCwrp6ekYMWIEgoOrB0lFR0cjLS0Nc+fOtWr/Pd8UobRcDy8ZIAB8/dNNRA1ujwt5lbgtzL/FA34MAjAYqi/MP16qkJpZ9HoBvUHgh5wKaLQG9A5pgx+yy2FuyESnIDm0egGdvvqi7iuXoUhde/Hu1tEH6kqDNN1ih0Bvab1MBlgzYuTz06Umy1ZO7oY+XS2nPyYispbDgkJBQQEUCoX0WalU4syZM1bvf+xnNQpLtNAbarN+1qQZrplk3JbO5lSgjY8M3t4yyL1kKPntQl5cpjMbEAL8vNC/RxvIvWXw8ZZVl1Mn8OWPtQNbugT7oE2ZHqXlerT188Lt4f64UqhFUZkO9w4Igq9chss39FAGeUnJ0bxk1ce+WaFHkVqPSREdEeDnxV4/RGQXDgsKBoPB6EImhGjShe31hf2k7YUQKFLrEODnjSqtATq9gFejWZwa/y4fuQzeXoCPtxe8bTRV2VKbHMVzKRStJx8/6+qZPK2uDgsKISEhOHnypPRZpVJBqTSdNMSSGzfKTNJc3Kys/bP5Fvam0drgGC3lysPmbY119Uysq2tobpoLh2VJveeee3Ds2DHcuHEDFRUVOHToECIiIhz19UREZAWHPSl06dIFCxYsQEJCArRaLSZMmIA777zTUV9PRERWcOg4hbi4OMTFxTnyK4mIqAk4yQ4REUkYFIiISMKgQEREErdJiFeT5rk1YF09E+vqmVy1rs0tl9tMx0lERPbH5iMiIpIwKBARkYRBgYiIJAwKREQkYVAgIiIJgwIREUkYFIiISMKgQEREEgYFIiKSuHxQ+OSTTzB27FhERUVhx44dzi5Og6ZNm4bY2FiMHz8e48ePx+nTpy2WPz09HXFxcYiKisKGDRuk5efOnUN8fDyio6OxbNky6HQ6AEBeXh6mTp2KmJgYzJo1C2VlZQCA0tJSzJw5E2PGjMHUqVOhUqnsWke1Wo1x48YhNzfXIfWoqqrCokWLMGbMGDz88MO4ePEigOopWV944QXExMRg7NixyMjIsHtdly5diqioKOn8fvbZZ079DWxl06ZNiI2NRWxsLNatW+fUOtn7vJqrq6ee12YTLuzatWvivvvuE0VFRaKsrEzExcWJCxcuOLtYZhkMBjFq1Cih1WqlZZbKX1FRISIjI8WlS5eEVqsVM2bMEIcPHxZCCBEbGytOnTolhBBi6dKlYseOHUIIIWbOnCn27dsnhBBi06ZNYt26dUIIIVatWiVee+01IYQQu3fvFk8++aTd6vj999+LcePGiQEDBojLly87pB5vvvmmWLFihRBCiOPHj4s//elPQgghDhw4IJ544gmh1+tFZmamGD16tNFvb+u6CiHEuHHjRH5+vtF2zvwNbOHrr78WEydOFBqNRlRVVYmEhATxySefeOR5NVfXQ4cOeeR5bQmXflJIT0/HiBEjEBwcjICAAERHRyMtLc3ZxTIrMzMTADBjxgw8+OCD2L59u8XynzlzBuHh4QgLC4NcLkdcXBzS0tJw5coVVFZWYtCgQQCA+Ph4pKWlQavV4sSJE4iOjjZaDgCHDx+WJi4aN24cvvzyS2i19plteteuXVi5cqU0t7Yj6nH48GE8+OCDAIBhw4bhxo0byMvLw5EjRzB27Fh4eXmhV69e6Nq1K06dOmW3ulZUVCAvLw9JSUmIi4vDxo0bYTAYnPob2IJCocCSJUvg6+sLHx8f9OnTB9nZ2R55Xs3VNS8vzyPPa0u4dJbUgoICKBQK6bNSqcSZM2ecWCLLSktLMXLkSKxYsQJarRYJCQkYM2aM2fKbq1d+fr7JcoVCgfz8fBQVFSEwMBByudxoOWD8G8nlcgQGBuLGjRvo0qWLzeu4Zs0ao8+OqIe5Y127dg0FBQXSBbvucnvVtbCwECNGjMDKlSsRFBSExMREfPTRRwgICHDab9CtW7cW17Nv377Sn7Ozs3HgwAH8+c9/9sjzaq6uO3bswPHjxz3uvLaESz8pGAwGyGS16V+FEEafXcngwYOxbt06BAUFoWPHjpgwYQI2btxotvyW6mVpubl6W/odhBDw8nLMaXVEPervU7Pc3HfYs95hYWHYvHkzlEol/P39MW3aNBw5csSpv4EtXbhwATNmzMDixYsRFhbm0ee1bl179+7t0ee1OZxfggaEhIQYvThVqVRGdxGu5OTJkzh27Jj0WQiB0NBQs+W3VK/6ywsLC6FUKtGxY0fcvHkTer3eaHug+g6msLAQAKDT6VBWVobg4GC71rWGI+rRpUsXFBQUmBwrJCTE7HJ7OX/+PA4ePCh9FkJALpc79TewlYyMDEyfPh1/+9vf8PDDD3v0ea1fV08+r83l0kHhnnvuwbFjx3Djxg1UVFTg0KFDiIiIcHaxzLp58ybWrVsHjUYDtVqN3bt348UXXzRb/oEDByIrKws5OTnQ6/XYt28fIiIiEBoaCj8/P6nHxZ49exAREQEfHx8MHToU+/fvBwCkpqZKv0NkZCRSU1MBAPv378fQoUPh4+PjkDo7oh6RkZHYs2cPgOrA6+fnh27duiEiIgKffPIJ9Ho9cnJykJ2djTvuuMNudRVC4Pnnn0dJSQm0Wi127tyJ0aNHO/U3sIWrV69izpw5SElJQWxsLADPPa/m6uqp57VF7Poa2wb27t0rYmNjRVRUlHj99dedXZwGbdiwQcTExIioqCixbds2IYTl8qenp4u4uDgRFRUl1qxZIwwGgxBCiHPnzok//vGPIjo6WixcuFBoNBohhBC5ubniz3/+sxgzZoyYMWOGKC4uFkIIUVRUJBITE8XYsWPFxIkTpZ4y9nTfffdJ32PvelRWVorFixeLsWPHioceekicPXtWCFHd2ys5OVmMHTtWjB07Vnz11Vd2r+v27dvFmDFjxOjRo8WLL74obeOs38AWnnvuOTFo0CDx4IMPSv+9//77HnleLdXVE89rS3DmNSIikrh08xERETkWgwIREUkYFIiISMKgQEREEgYFIiKSMCgQEZGEQYGIiCQMCkREJPl/RhDFSMeQUxAAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1485,12 +1487,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1533,7 +1537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1556,7 +1560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1669,7 +1673,7 @@
        "[2 rows x 22 columns]"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1709,7 +1713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -1721,21 +1725,30 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
+       "<Figure size 720x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
+    "from matplotlib.ticker import MaxNLocator\n",
     "ncomps = 5\n",
     "svd = TruncatedSVD(n_components=ncomps)\n",
     "svd_fit = svd.fit(rescaledDataset)\n",
+    "plt_data = pd.DataFrame(svd_fit.explained_variance_ratio_.cumsum()*100)\n",
+    "plt_data.index = np.arange(1, len(plt_data) + 1)\n",
     "Y_pred = svd.fit_transform(rescaledDataset)\n",
-    "ax = pd.Series(svd_fit.explained_variance_ratio_.cumsum()).plot(kind='line', figsize=(10, 3))\n",
+    "ax = plt_data.plot(kind='line', figsize=(10, 4))\n",
+    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+    "ax.set_xlabel(\"Eigenvalues\")\n",
+    "ax.set_ylabel(\"Percentage Explained\")\n",
+    "ax.legend(\"\")\n",
     "print('Variance preserved by first 5 components == {:.2%}'.format(svd_fit.explained_variance_ratio_.cumsum()[-1]))"
    ]
   },