insurance-suggested-rates

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insurance-suggested-rates

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main.py
README.md
requirements.txt
test.txt

// repository documentation

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# Insurance Suggested Rates - Premium Prediction

A machine learning project to predict insurance suggested rates based on customer attributes.

## Project Overview

This project uses a **Linear Regression** model to predict insurance suggested rates based on:
- Age
- Gender
- BMI (Body Mass Index)
- Number of children
- Smoking status
- Region

## Project Structure

```
insurance-suggested-rates/
├── main.py          # Main script with model training and prediction
├── requirements.txt # Python dependencies
├── README.md        # This file
└── models/          # Saved model files (created after running)
    ├── model.pkl
    └── encoders.pkl
```

## Setup

1. **Create virtual environment** (optional but recommended):
   ```bash
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   ```

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

## Usage

### Train the Model

```bash
python main.py
```

This will:
- Create sample training data
- Train a Linear Regression model
- Save the model and encoders to the `models/` directory

### Make Predictions

After training, you can use the `predict_premium()` function:

```python
from main import predict_premium

premium = predict_premium(
    age=30,
    gender='female',
    bmi=25.0,
    children=1,
    smoker='no',
    region='northeast'
)
print(f"Predicted Premium: ${premium:,.2f}")
```

## Model Details

- **Algorithm**: Linear Regression
- **Features**: 6 (age, gender, bmi, children, smoker, region)
- **Target**: Premium amount

## License

MIT

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