Customer Churn Prediction and Explainable Retention Strategies in the Telecom Sector

A telecom customer churn prediction system with explainable retention strategies.

A telecom customer churn prediction system with explainable retention strategies. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from it.

It suits learners who want to turn raw data into decisions: collecting and cleaning data, exploring it, modelling it and presenting the findings in a dashboard or report.

The main parts are explanations of each result (feature importance or visual evidence), data collection and cleaning, exploratory analysis and feature building, modelling and validation, and an interactive dashboard of the results. The reference build uses SHAP, Python, Pandas, Apache Spark, and Power BI, with a clear project structure, setup instructions and sample data so that it can be run and extended.