Medical Cost Prediction Project

This project analyzes and predicts medical costs using machine learning techniques.

Live Demo

🔗 Try the Live Application

The application is deployed on Streamlit Cloud and provides instant medical insurance cost predictions in both USD and INR.

Setup Instructions

1. Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

3. Run Jupyter Notebook:

jupyter notebook

4. Open notebooks/Medical_Cost_Prediction.ipynb in your browser and run the cells sequentially.

Project Structure

• notebooks/: Contains Jupyter notebooks with analysis
  • Medical_Cost_Prediction.ipynb: Main analysis notebook
• app.py: Streamlit web application for cost prediction
• models/: Contains trained machine learning models

Data Description

The project analyzes medical cost data with features including:

• Age
• BMI (Body Mass Index)
• Number of children
• Medical charges

Features

• Predicts medical insurance costs in USD and INR
• Takes into account:
  • Age
  • BMI
  • Smoking status
  • Number of children
  • Region
  • Gender

Model

Currently using Linear Regression with features:

• Age
• BMI
• Smoking status

Usage

You can either:

1. Use the live application
2. Run locally using streamlit run app.py