Enterprise-grade Telecom Customer Churn Prediction system blending advanced machine learning (XGBoost), real-time Flask API deployment, and interactive Streamlit dashboards to enable data-driven customer retention strategies.
This project uses real-world telecom customer data to predict churn behavior using machine learning. It includes data cleaning, exploratory data analysis (EDA), feature engineering, model training (Logistic Regression and Random Forest), and strategic business recommendations. The final model is ready for deployment in customer retention systems.
this project focuses on predicting telecom customer churn using supervised machine learning models. by analyzing historical data such as contract type, internet service usage, and billing method, we aim to identify customers who are at risk of leaving the company.
Random Forest Classifier for Customer Churn Prediction
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