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Exercise

Train/test split + computing accuracy

Now that you have learned about the importance of splitting your data into training and test sets, it's time to practice doing this on the churn_df dataset!

NumPy arrays have been created for you containing the features as X and the target variable as y. You will split them into training and test sets, fit a KNN classifier to the training data, and then compute its accuracy on the test data using the .score() method.

Instructions

100 XP
  • Import train_test_split from sklearn.model_selection.
  • Split X and y into training and test sets, setting test_size equal to 20%, random_state to 42, and ensuring the target label proportions reflect that of the original dataset.
  • Fit the knn model to the training data.
  • Compute and print the model's accuracy for the test data.