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.

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.

Exercise

Train/test split + computing accuracy

Instructions