Customer Churn Prediction System

A comprehensive machine learning solution for predicting customer churn and driving retention strategies.

Python numpy Mathplotlib Seaborn Pandas Scikit-learn Decision Tree Random Forest Logistic Regression XGBoost

Project Screenshots

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Project Overview

A sophisticated machine learning system that predicts customer churn, helping businesses proactively retain at-risk customers. Built with Python, Scikit-learn, and Random Forest, it features advanced data preprocessing, feature engineering, multiple ML models, comprehensive evaluation metrics, and actionable business insights for retention strategies.

Key Features

  • Advanced data preprocessing and cleaning
  • Feature engineering for payment, service usage, and customer behavior
  • Machine learning models: Logistic Regression, Decision Tree, Random Forest, XGBoost
  • Cross-validation, hyperparameter tuning, and model comparison
  • Comprehensive evaluation metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC
  • Business intelligence: churn probability scoring and risk segmentation
  • Insights for contract optimization, payment automation, and service bundling
  • Strategic recommendations for proactive retention campaigns
GitHub Repository