Telecom-Churn-Prediction and Customer-Churn-Prediction

About Telecom-Churn-Prediction

ChaitanyaC22/Telecom-Churn-Prediction

In this project, data analytics is used to analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn, and identify the main indicators of churn. The project focuses on a four-month window, wherein the first two months are the ‘good’ phase, the third month is the ‘action’ phase, while the fourth month is the ‘churn’ phase. The business objective is to predict the churn in the last i.e. fourth month using the data from the first three months.

This project helps telecom companies predict which high-value prepaid customers are likely to switch providers. By analyzing past customer usage and recharge data, it identifies individuals at high risk of churn, providing insights that allow for targeted retention efforts. It's designed for business analysts and customer retention teams in the telecom sector to proactively address customer attrition.

About Customer-Churn-Prediction

JavedFazlulahF/Customer-Churn-Prediction

📊 Predict customer churn in telecom using machine learning to enhance retention strategies and drive better business outcomes.