This project predicts insurance costs based on user inputs like age, BMI, number of children, smoking status, gender, and region. It uses Linear Regression to estimate medical insurance charges.

✅ Machine Learning Model: Trained on insurance dataset using LinearRegression from sklearn. ✅ Web Interface: Users can input details via a Flask-based web app. ✅ Interactive UI: Bootstrap-powered form for a professional look. ✅ Model Deployment: Deployed locally using Flask.

The model is trained on insurance.csv, which includes:

• age: Age of the person
• bmi: Body Mass Index
• children: Number of children
• sex: Male (0) or Female (1)
• smoker: No (0) or Yes (1)
• region: Encoded as 0 (Northwest), 1 (Southeast), 2 (Southwest)
• charges: Insurance cost (target variable)

Clone the repository and install the required dependencies:

git clone https://github.com/your-repo/insurance-prediction.git
cd insurance-prediction
pip install -r requirements.txt

If you need to retrain the model, run:

python train_model.py

This will train the LinearRegression model and save it as insurance_model.pkl.

Start the Flask app:

python app.py

Visit http://127.0.0.1:5000/ in your browser.

Ensure Flask is installed and run:

python app.py