Train/test split + computing accuracy

It's time to practice splitting your data into training and test sets with the churn_df dataset!

NumPy arrays have been created for you containing the features as X and the target variable as y.

This exercise is part of the course

Supervised Learning with scikit-learn

Exercise instructions

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.

Hands-on interactive exercise

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

# Import the module
from ____ import ____

X = churn_df.drop("churn", axis=1).values
y = churn_df["churn"].values

# Split into training and test sets
X_train, X_test, y_train, y_test = ____(____, ____, test_size=____, random_state=____, stratify=____)
knn = KNeighborsClassifier(n_neighbors=5)

# Fit the classifier to the training data
____

# Print the accuracy
print(knn.score(____, ____))

Edit and Run Code

This exercise is part of the course

Supervised Learning with scikit-learn

IntermediateSkill Level

4.8+

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In this chapter, you'll be introduced to classification problems and learn how to solve them using supervised learning techniques. You'll learn how to split data into training and test sets, fit a model, make predictions, and evaluate accuracy. You’ll discover the relationship between model complexity and performance, applying what you learn to a churn dataset, where you will classify the churn status of a telecom company's customers.

Exercise 1: Machine learning with scikit-learn Exercise 2: Binary classification Exercise 3: The supervised learning workflow Exercise 4: The classification challenge Exercise 5: k-Nearest Neighbors: Fit Exercise 6: k-Nearest Neighbors: Predict Exercise 7: Measuring model performance Exercise 8: Train/test split + computing accuracy

Current Exercise

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In this chapter, you will be introduced to regression, and build models to predict sales values using a dataset on advertising expenditure. You will learn about the mechanics of linear regression and common performance metrics such as R-squared and root mean squared error. You will perform k-fold cross-validation, and apply regularization to regression models to reduce the risk of overfitting.

Exercise 1: Introduction to regression Exercise 2: Creating features Exercise 3: Building a linear regression model Exercise 4: Visualizing a linear regression model Exercise 5: The basics of linear regression Exercise 6: Fit and predict for regression Exercise 7: Regression performance Exercise 8: Cross-validation Exercise 9: Cross-validation for R-squared Exercise 10: Analyzing cross-validation metrics Exercise 11: Regularized regression Exercise 12: Regularized regression: Ridge Exercise 13: Lasso regression for feature importance

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Exercise 1: How good is your model?Exercise 2: Deciding on a primary metric Exercise 3: Assessing a diabetes prediction classifier Exercise 4: Logistic regression and the ROC curve Exercise 5: Building a logistic regression model Exercise 6: The ROC curve Exercise 7: ROC AUC Exercise 8: Hyperparameter tuning Exercise 9: Hyperparameter tuning with GridSearchCV Exercise 10: Hyperparameter tuning with RandomizedSearchCV

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Exercise 1: Preprocessing data Exercise 2: Creating dummy variables Exercise 3: Regression with categorical features Exercise 4: Handling missing data Exercise 5: Dropping missing data Exercise 6: Pipeline for song genre prediction: I Exercise 7: Pipeline for song genre prediction: II Exercise 8: Centering and scaling Exercise 9: Centering and scaling for regression Exercise 10: Centering and scaling for classification Exercise 11: Evaluating multiple models Exercise 12: Visualizing regression model performance Exercise 13: Predicting on the test set Exercise 14: Visualizing classification model performance Exercise 15: Pipeline for predicting song popularity Exercise 16: Congratulations