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

AmirhosseinHonardoust/Customer-Churn-Prediction

Customer churn prediction with Python using synthetic datasets. Includes data generation, feature engineering, and training with Logistic Regression, Random Forest, and Gradient Boosting. Improved pipeline applies hyperparameter tuning and threshold optimization to boost recall. Outputs metrics, reports, and charts.

This project helps marketing managers, product managers, and customer success teams identify customers likely to leave your service. You provide historical customer data, including demographics, spending habits, and interaction history. It then generates models, performance reports, and visualizations that highlight key churn drivers and predict who is at risk.