Insurance Predictor For Nano-Entrepreneurs

Project Description

This project provides a web-based application to predict health insurance costs based on various personal and health-related factors. It is designed to assist nano-entrepreneurs in estimating potential insurance expenses, enabling better financial planning. The application uses a machine learning model trained on a dataset of health insurance information.

Features

• Web Interface: User-friendly web form for inputting personal details.
• Insurance Cost Prediction: Predicts insurance charges based on age, gender, BMI, number of children, smoking status, and region.
• Data Validation: Basic server-side validation for input parameters.
• Machine Learning Model: Utilizes a trained scikit-learn model for predictions.

Technologies Used

• Python: Core programming language.
• Flask: Web framework for building the application.
• scikit-learn: Machine learning library for model training and prediction.
• Pandas: Data manipulation and analysis.
• NumPy: Numerical operations.
• Materialize CSS: Frontend framework for responsive and modern design.
• Jupyter Notebook: For data analysis and exploration (6_Health Insurance Cost Analysis and Prediction.ipynb).

Setup and Installation

To set up and run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/TashonBraganca/Insurance-Predictor-For-Nano-Entrepreneurs.git
   cd Insurance-Predictor-For-Nano-Entrepreneurs

2. Create a virtual environment (recommended):

   python -m venv venv
   # On Windows
   .\venv\Scripts\activate
   # On macOS/Linux
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Ensure the model is trained: The insurance_model.pkl file should be present in the root directory. If it's missing or you wish to retrain the model, run:

   python train_model.py

   This will train the model using insurance.csv and save it as insurance_model.pkl.

Usage

Running the Web Application

To start the Flask web application:

python app.py

The application will typically run on http://127.0.0.1:5000/. Open this URL in your web browser to access the insurance predictor.

Retraining the Model

If you update the insurance.csv dataset or want to retrain the model for any reason, simply run the train_model.py script:

python train_model.py

This will overwrite the existing insurance_model.pkl with the newly trained model.

Project Structure

Insurance-Predictor-For-Nano-Entrepreneurs/
├── app.py                      # Flask web application
├── train_model.py              # Script to train and save the ML model
├── insurance_model.pkl         # Trained machine learning model (generated by train_model.py)
├── insurance.csv               # Dataset used for model training
├── requirements.txt            # Python dependencies
├── 6_Health Insurance Cost Analysis and Prediction.ipynb # Jupyter Notebook for data analysis
├── README.md                   # Project README file
├── sampleImages/               # Contains sample images related to analysis/deployment
│   ├── AgevsCharges.png
│   ├── Cor.png
│   ├── deployments.png
│   └── doc.gif
├── static/                     # Static files (CSS, JS) for the web application
│   ├── css/
│   │   ├── materialize.css
│   │   └── materialize.min.css
│   └── js/
│       ├── materialize.js
│       └── materialize.min.js
└── templates/                  # HTML templates for the web application
    ├── home.html               # Input form for predictions
    └── op.html                 # Output page for displaying predictions

Model Details

The machine learning model used is a Linear Regression model combined with Polynomial Features (degree 2). This approach allows the model to capture non-linear relationships between the input features and the insurance charges.

Data Preprocessing:

• Categorical features (sex, smoker, region) are converted into numerical representations.
• sex and smoker are mapped to 0s and 1s.
• region is one-hot encoded using pandas.get_dummies with drop_first=True to avoid multicollinearity.

Contributing

Feel free to fork this repository, make improvements, and submit pull requests.

License

This project is open-source and available under the MIT License.