Telecom-Customer-Churn-Analysis-Prediction and Telecom-Churn-Prediction

About Telecom-Customer-Churn-Analysis-Prediction

VibolvatanakPOCH/Telecom-Customer-Churn-Analysis-Prediction

Telecom Customer Churn Analysis & Prediction project uses Gradient Boosting for precise predictions, Power BI for churn pattern visualizations, and Streamlit for interactive insights. With robust code and meticulous data preprocessing, stakeholders access accurate predictions to optimize retention and drive profitability.

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.