lint

logistic-regression.py

@@ -86,31 +86,32 @@ def prepare_data():
     return train_test_split(x, y, test_size=0.30, stratify=y, random_state=1)
 
 
-def main():
+def logistic_regression():
     """
     - create training and test data sets
     - create a Logistic Regression model
     - train the model
     - generate confusion matrix and f-score for the training set
     - generate confusion matrix and f-score for the test set
     """
+    print("Prepare data")
     x_train, x_test, y_train, y_test = prepare_data()
 
-    # Fit a logistic regression model
+    print("train model")
     model = LogisticRegression()
     model.fit(x_train, y_train)
 
     # Set the optimal threshold (refer to the Jupyter Notebook to see how we arrived at 42)
     optimal_threshold = 0.42
 
-    # Create a confusion matrix for the training data
+    print("model training data and measure results")
     y_pred_train = model.predict_proba(x_train)
     metrics_score(y_train, y_pred_train[:, 1] > optimal_threshold)
 
-    # Create a confusion matrix for the test data
+    print("model test data and measure results")
     y_pred_test = model.predict_proba(x_test)
     metrics_score(y_test, y_pred_test[:, 1] > optimal_threshold)
 
 
 if __name__ == "__main__":
-    main()
+    logistic_regression()