Medical_Insurance_Cost_Prediction-Streamlit

Project Structure

• insurance_model.pkl # Saved model using Pickle
• insurance_app.py # Streamlit application code
• README.md # Project documentation
• requirements.txt # Required Python packages
• Dataset/
• insurance.csv # Dataset for training

Features

• Interactive web-based application using Streamlit.
• Predicts insurance charges based on user inputs.
• Uses ColumnTransformer and Pipeline for efficient preprocessing.
• Trained model saved as a .pkl file for deployment and reusability.

Dataset

The project uses the insurance.csv dataset with the following features:

• age: Age of the policyholder.
• sex: Gender of the policyholder (male or female).
• bmi: Body Mass Index.
• children: Number of children or dependents.
• smoker: Smoking status (yes or no).
• region: Residential region (southeast, southwest, northeast, northwest).
• charges: Insurance charges (target variable).