Churn PySpark

This project involves building and training a machine learning model using PySpark to predict customer churn. The dataset contains customer attributes and churn status, and it goes through data exploration and preprocessing steps initially. Then, machine learning algorithms such as logistic regression and gradient boosting machine (GBM) are used to build the model. Finally, various metrics are used to evaluate the model's performance, and model hyperparameters are tuned using CrossValidator.