This project predicts whether a telecom customer is likely to churn based on service usage and demographic data. It involves data preprocessing, feature engineering, and training a Random Forest classifier. The final model is deployed using a Streamlit web app.
- Predict customer churn using key inputs like tenure, contract type, monthly charges, etc.
- Clean and user-friendly web interface built with Streamlit.
- Interactive model predictions with real-time user input.
- Trained model and features stored using
joblib.
- Python, Pandas, Scikit-learn
- Streamlit (for deployment)
- Joblib (for saving model artifacts)
- Clone the repo
- Install dependencies:
pip install -r requirements.txt - Run:
streamlit run app.py