Medical Insurance Cost Prediction

A Machine Learning web application built using Python, Scikit-learn, and Streamlit that predicts medical insurance charges based on personal and health-related information.

Live Demo

https://medical-insurance-cost-predictionn.streamlit.app/

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Project Overview

This project uses a Random Forest Regression model to estimate medical insurance costs using the following features:

• Age
• Gender
• BMI
• Number of Children
• Smoking Status
• Region

The trained model is deployed using Streamlit, allowing users to interactively enter details and receive instant insurance cost predictions.

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Dataset

• Medical Insurance Dataset
• Records: 2,772
• Features: 6
• Target: Insurance Charges

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Machine Learning Workflow

• Data Cleaning
• Exploratory Data Analysis (EDA)
• Feature Encoding
• Model Training
• Model Evaluation
• Hyperparameter Tuning
• Streamlit Deployment

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Models Compared

• Linear Regression
• Decision Tree Regressor
• Random Forest Regressor
• Gradient Boosting Regressor

Random Forest achieved the best performance and was selected as the final model.

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Results

Metric	Value
R² Score	0.95
RMSE	2767.29
MAE	1317.32

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Screenshots

Home Page

Prediction

Feature Importance

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Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Streamlit
• Matplotlib
• Seaborn

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Project Structure

Medical_Insurance_Streamlit_App/
│
├── images/
├── report/
├── app.py
├── train_model.py
├── model.pkl
├── insurance.csv
├── requirements.txt
└── README.md

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Run Locally

Install dependencies:

pip install -r requirements.txt

Run the Streamlit app:

python -m streamlit run app.py

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Author

Zaara Khan

B.Tech CSE Student