https://github.com/ritesh-cloud/ai-powered-customer-churn-prediction

AI-Powered Customer Churn Prediction is a Flask-based web application that leverages machine learning to help businesses predict whether a customer is likely to leave (churn). Built with a clean and responsive UI using Bootstrap, this tool allows users to input key customer details — such as tenure, monthly charges, contract type.
https://github.com/ritesh-cloud/ai-powered-customer-churn-prediction

css flask html ml pickle python scikitlearn-machine-learning xgboost

Last synced: 8 months ago
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AI-Powered Customer Churn Prediction is a Flask-based web application that leverages machine learning to help businesses predict whether a customer is likely to leave (churn). Built with a clean and responsive UI using Bootstrap, this tool allows users to input key customer details — such as tenure, monthly charges, contract type.

• Host: GitHub
• URL: https://github.com/ritesh-cloud/ai-powered-customer-churn-prediction
• Owner: Ritesh-cloud
• License: mit
• Created: 2025-10-04T16:20:26.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2025-10-04T16:38:46.000Z (8 months ago)
• Last Synced: 2025-10-04T18:26:02.900Z (8 months ago)
• Topics: css, flask, html, ml, pickle, python, scikitlearn-machine-learning, xgboost
• Language: HTML
• Homepage:
• Size: 16.6 KB
• Stars: 1
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
🔮 AI-Powered Customer Churn Prediction

Predict customer churn with confidence using machine learning and a clean Flask web app. This project allows businesses to proactively identify customers at risk of leaving — before it’s too late.

🧠 What Is Customer Churn?

Customer churn refers to the percentage of customers who stop using a company’s product or service over a given time period. Predicting churn helps in improving retention, reducing losses, and increasing profitability.

💡 Features

✅ Predict churn based on 19 input features

✅ Clean and responsive UI with Bootstrap

✅ Machine learning model trained and integrated

✅ Preprocessing pipeline for real-time predictions

✅ Probability-based feedback with clear messaging

🖥️ Tech Stack

Layer	Tools Used

Frontend	HTML, CSS, Bootstrap

Backend	Flask (Python)

ML Model	Pickle (trained model), Scikit-learn

Data Handling	Pandas, Custom Preprocessing

🔍 Model Info

Trained on Telco Customer Churn Dataset (Kaggle)

Preprocessing: Label encoding, handling missing values

Algorithm: RandomForestClassifier (customizable)

Evaluation: Accuracy, ROC-AUC, Precision-Recall

🤝 Contributing

Contributions are welcome!

Feel free to fork the repo and open a PR with improvements 🚀

📃 License

MIT License © 2025 Your Name

🌟 Show Your Support

If you found this project useful or interesting, give it a ⭐ and share it with others!