Customer churn prevention is a challenging problem for almost every product and service company. Predicting customer churn can provide valuable insights into user behavior and help companies identify key trends that could indicate which customers are at risk of churning. In this project, we aim to analyze web log data of a music streaming service to predict customer churn using PySpark and SparkML algorithms.
We have access to user event data, which contains every interaction of every user with the application. This includes events such as when a user goes to the Home page, listens to a song, thumbs up a song, etc.
The project involves the following steps:
Exploratory data analysis to identify key variables of interest. Feature engineering to create useful features for a classification model for churn. Experimenting with different model algorithms such as Logistic regression, random forest, gradient boosting, and decision trees to evaluate the problem. Evaluating the classification model using standard metrics for binary output data - accuracy and F1-score.
PySpark SparkML Library
We expect to identify key variables that reveal a substantial difference between customers that churn versus those that don't. By utilizing machine learning techniques, we can create a model that accurately predicts customer churn. This can provide valuable insights into user behavior, help identify key trends that could indicate which customers are at risk of churning, and incentivize customers to remain loyal to the service.
Predicting customer churn is a valuable tool for any customer-facing business. By utilizing PySpark and SparkML algorithms, we can create a model that accurately predicts customer churn and provides valuable insights into user behavior. This can help companies identify key trends and incentivize customers to remain loyal to the service, resulting in a loyal customer base and increased revenue.