Churn-predictor

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Churn-predictor

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# SaaS Churn Prediction System

Advanced churn prediction system using FastAPI, SHAP, and XGBoost/RandomForest.
Runs purely offline on CPU.

## Project Structure

```
├── app/                # FastAPI application
├── config/             # Configuration files
├── data/               # Dataset storage
├── models/             # Saved models and metrics
├── src/                # Source code
│   ├── data/           # Data loading
│   ├── evaluation/     # Metrics & Plotting
│   ├── explainability/ # SHAP utilities
│   ├── features/       # Feature engineering
│   └── models/         # Training pipeline
├── requirements.txt
├── run_pipeline.py     # Automation script
└── README.md
```

## Setup

1. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```

## Training

Run the full training pipeline:
```bash
python run_pipeline.py
```

This will:
- Load `data/churn2.csv`
- Preprocess and engineer features (Ratio features, OHE)
- Train Logistic Regression, Random Forest, and XGBoost (with SMOTE)
- Optimize hyperparameters
- Save the best model to `models/best_model.pkl`
- Save metrics to `models/metrics.json`

## API Usage

Start the FastAPI server:
```bash
uvicorn app.main:app --host 0.0.0.0 --port 8000
```

### Endpoints

- `GET /health`: Check system status.
- `POST /predict`: Get churn probability.
- `POST /explain`: Get SHAP explanation.

### Example Request

```json
POST /predict
{
  "CreditScore": 619,
  "Geography": "France",
  "Gender": "Female",
  "Age": 42,
  "Tenure": 2,
  "Balance": 0.0,
  "NumOfProducts": 1,
  "HasCrCard": 1,
  "IsActiveMember": 1,
  "EstimatedSalary": 101348.88
}
```

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