Telecom-Churn-Prediction and Telecom-Churn-Analysis
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 Telecom-Churn-Analysis
AliAmini93/Telecom-Churn-Analysis
Developed a churn prediction model using XGBoost, with comprehensive data preprocessing and hyperparameter tuning. Applied SHAP for feature importance analysis, leading to actionable business insights for targeted customer retention.
This project helps telecom companies understand why customers leave by analyzing their usage patterns and demographics. It takes customer data like age, contract type, and service usage, and predicts which customers are likely to churn. This allows customer retention specialists or marketing managers to proactively target at-risk customers with specific offers to keep them.
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