Using a pipeline

Now that you have our pipeline defined, aka combining a logistic regression with a SMOTE method, let's run it on the data. You can treat the pipeline as if it were a single machine learning model. Our data X and y are already defined, and the pipeline is defined in the previous exercise. Are you curious to find out what the model results are? Let's give it a try!

This exercise is part of the course

Fraud Detection in Python

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Exercise instructions

Split the data 'X'and 'y' into the training and test set. Set aside 30% of the data for a test set, and set the random_state to zero.
Fit your pipeline onto your training data and obtain the predictions by running the pipeline.predict() function on our X_test dataset.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Split your data X and y, into a training and a test set and fit the pipeline onto the training data
X_train, X_test, y_train, y_test = ____

# Fit your pipeline onto your training set and obtain predictions by fitting the model onto the test data 
pipeline.fit(____, ____) 
predicted = pipeline.____(____)

# Obtain the results from the classification report and confusion matrix 
print('Classifcation report:\n', classification_report(y_test, predicted))
conf_mat = confusion_matrix(y_true=y_test, y_pred=predicted)
print('Confusion matrix:\n', conf_mat)

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