A Machine Learning project that predicts whether a customer will churn (leave the bank) or stay, based on historical customer data.
This model helps banks identify at-risk customers early and take proactive measures to improve customer retention.
Customer churn is a major concern in the banking sector — acquiring a new customer costs far more than retaining an existing one.
This project applies supervised machine learning to predict customer churn using features such as credit score, age, tenure, balance, and transaction activity.
- Python 3.x
- Pandas – Data cleaning and manipulation
- Scikit-learn – Model building and evaluation
- NumPy – Numerical operations
- Matplotlib / Seaborn – Data visualization (optional)
- Jupyter Notebook or VS Code – Development environment