The dataset includes different information about customers. The objective is to predict customer churn from the data. The input data is highly imbalanced consisting 150 churn (i.e., churn = 1) and 750 no churn (i.e., churn = 0) customers. Check the customer_churn.csv dataset for details.

• MLlib and PySpark is used to build the model.
• Feature vectorization is performed to convert the categorical features.
• Random undersampling is performed to the majority class (i.e., No Churn) and random oversampling is performed to the minority class (i.e., Churn) to balance the class distribution.
• Logistic regression, Random Forest and Gradient Boosting Tree are applied to the balanced data.
• The best performance is achieved for the Gradient Boosting Tree with AUC (Area Under Curve) = 0.92.

This Project’s GitHub Repository

• Big Data
• Classification
• Spark
• MLib