tutorial creditcard fraud

Dockerfile

@@ -40,6 +40,7 @@ VOLUME /var/lib/mysql
 COPY example/datasets/popularize_churn.sql \
      example/datasets/popularize_iris.sql \
      example/datasets/popularize_boston.sql \
+     example/datasets/popularize_creditcardfraud.sql \
      example/datasets/create_model_db.sql \
      /docker-entrypoint-initdb.d/
 

doc/examples/tutorial_creditcardfraud.md

@@ -0,0 +1,46 @@
+# The Credit Card Fraud Detection Example
+
+The sample data already loaded in MySQL comes from [Kaggle](https://www.kaggle.com/mlg-ulb/creditcardfraud). To train the model using the full dataset, you need to download the dataset and load the dataset into MySQL manually.
+
+You can verify the sample data content in MySQL using:
+
+```sql
+%%sqlflow
+SELECT * from creditcard.creditcard limit 5;
+```
+
+## Train a DNN Model Using SQLFlow
+
+Once your dataset is prepared, you can run the below SQL statement to start training.
+Note that SQLFlow will automatically split the dataset into training and validation
+sets, the output of evaluation result is calculated using the validation set.
+
+```sql
+%%sqlflow
+SELECT * from creditcard.creditcard
+TRAIN DNNClassifier
+WITH model.n_classes=2, model.hidden_units=[128,32], train.epoch=100
+COLUMN time,v1,v2,v3,v4,v5,v6,v7,v8,v9,v10,v11,v12,v13,v14,v15,v16,v17,v18,v19,v20,v21,v22,v23,v24,v25,v26,v27,v28,amount
+LABEL class
+INTO creditcard.creditcard_deep_model;
+```
+
+## Run Predict
+
+We can use the trained model to predict new data, e.g. we can choose some positive sample in the dataset
+to do predict:
+
+```sql
+%%sqlflow
+SELECT * from creditcard.creditcard
+WHERE class=1
+PREDICT creditcard.predict.class
+USING creditcard.creditcard_deep_model;
+```
+
+Then we can get the predict result using:
+
+```sql
+%%sqlflow
+SELECT * from creditcard.predict;
+```