A machine learning project to predict customer churn using synthetic data and various ML models.
- Logistic Regression, Random Forest, SVM, and Gradient Boosting models.
- Synthetic dataset generated with customer information.
- Preprocessing pipeline with feature engineering.
- Model evaluation with accuracy and classification reports.
CustomerChurnPrediction/ ├── churn_prediction.py # Main script for ML pipeline ├── data/ │ └── churn_data.csv # Dataset (generated) ├── README.md # Project documentation ├── requirements.txt # Dependencies
- Clone the repository:
git clone <repo-url> cd CustomerChurnPrediction
pip install -r requirements.txt
python churn_prediction.py
The results, including accuracy and classification reports, are saved in results.txt.