[FSTORE-1196][APPEND] Link from README to Neo4j tutorial (#235)

Link from README to Neo4j tutorial
logicalclocks · Feb 19, 2024 · eff9074 · eff9074
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diff --git a/README.md b/README.md
@@ -62,6 +62,7 @@ In order to understand the tutorials you need to be familiar with general concep
     - [DBT Tutorial with BigQuery](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/dbt_bq): Perform feature engineering in DBT on BigQuery.
     - [WandB](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/wandb): Build a machine learning model with Weights & Biases.
     - [Great Expectations](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/great_expectations): Introduction to Great Expectations concepts and classes which are relevant for integration with the Hopsworks MLOps platform.
+    - [Neo4j](integrations/neo4j): Perform Anti-money laundering (AML) predictions using Neo4j Graph representation of transactions.
     - [Monitoring](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/monitoring): How to implement feature monitoring in your production pipeline.
     - [Bytewax](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/bytewax): Real time feature computation using Bytewax.
     - [Apache Beam](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/java/beam): Real time feature computation using Apache Beam, Google Cloud Dataflow and Hopsworks Feature Store.

diff --git a/integrations/neo4j/2_training_pipeline.ipynb b/integrations/neo4j/2_training_pipeline.ipynb
@@ -101,22 +101,21 @@
    "outputs": [],
    "source": [
     "# Select features for training data\n",
-    "selected_features = transactions_monthly_fg.select(\n",
-    "    [\n",
-    "        \"monthly_in_count\", \n",
-    "        \"monthly_in_total_amount\", \n",
-    "        \"monthly_in_mean_amount\", \n",
-    "        \"monthly_in_std_amount\", \n",
-    "        \"monthly_out_count\", \n",
-    "        \"monthly_out_total_amount\", \n",
-    "        \"monthly_out_mean_amount\", \n",
-    "        \"monthly_out_std_amount\",\n",
-    "    ]\n",
-    ").join(\n",
-    "    graph_embeddings_fg.select([\"graph_embeddings\"]),\n",
-    ").join(\n",
-    "    party_fg.select([\"type\", \"is_sar\"]), \n",
-    ")"
+    "selected_features = party_fg.select([\"type\", \"is_sar\"]).join(\n",
+    "    transactions_monthly_fg.select(\n",
+    "        [\n",
+    "            \"monthly_in_count\", \n",
+    "            \"monthly_in_total_amount\", \n",
+    "            \"monthly_in_mean_amount\", \n",
+    "            \"monthly_in_std_amount\", \n",
+    "            \"monthly_out_count\", \n",
+    "            \"monthly_out_total_amount\", \n",
+    "            \"monthly_out_mean_amount\", \n",
+    "            \"monthly_out_std_amount\",\n",
+    "        ]\n",
+    "    ).join(\n",
+    "        graph_embeddings_fg.select([\"graph_embeddings\"]),\n",
+    "    )"
    ]
   },
   {