Medical Insurance Cost Prediction

This repository contains a machine learning project that predicts medical insurance costs based on user inputs such as age, gender, BMI, number of children, smoking status, and region.

Features

• Implements multiple machine learning models:

  • Linear Regression
  • Random Forest Regressor
  • Decision Tree Regressor

• Provides a user-friendly Streamlit web interface to interact with the models.

• Exposes a RESTful API to make predictions using the trained models.

Project Structure

├── app.py                  # Streamlit app interface
├── main.py                 # Core logic and model loading
├── api.py                  # RESTful API using FastAPI
├── model/                  # Directory containing trained model pickle files
│   ├── linear_model.pkl
│   ├── random_model.pkl
│   └── descision_model.pkl
├── static/                 # Any frontend assets if needed
├── requirements.txt        # Python dependencies
└── README.md

Installation

1. Clone the repository:

git clone https://github.com/pranjaykumar926/Medical-Insurance-Cost-Prediction.git
cd Medical-Insurance-Cost-Prediction

2. Install dependencies:

pip install -r requirements.txt

Running the Streamlit App

streamlit run app.py

Running the API Server

uvicorn api:app --reload

The API will be available at http://127.0.0.1:8000 with automatic documentation at http://127.0.0.1:8000/docs (Swagger UI).

API Endpoints

GET /

Returns a welcome message.

POST /predict

Accepts JSON input and returns the predicted insurance cost.

Input JSON:

{
  "age": 29,
  "sex": "male",
  "bmi": 27.9,
  "children": 0,
  "smoker": "yes",
  "region": "southwest",
  "model": "linear"
}

Supported model values: linear, random, descision

Output JSON:

{
  "prediction": 16884.92
}

Author

Pranjay Kumar GitHub

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Feel free to fork this repository and contribute to the project.