🏥 Health Care Premium Prediction

A machine learning-based project that predicts health care premium costs based on various user-provided criteria. This project demonstrates data preprocessing, regression modeling, and deployment with a web-based interface using Streamlit.

🚀 Features

• Load and process premium data from Excel files using Pandas
• Clean and scale data with MinMaxScaler
• Train prediction models using Scikit-learn regression algorithms
• Achieve up to 90% prediction accuracy
• Build an interactive UI with Streamlit
• Deploy the application on Streamlit Cloud

📊 Workflow

1. Data Ingestion

   • Read past health insurance premium data from Excel files
   • Explore and preprocess the data using Pandas and NumPy

2. Data Cleaning & Scaling

   • Handle missing or inconsistent values
   • Apply MinMaxScaler to normalize feature values

3. Model Training

   • Use regression models from Scikit-learn (e.g., Linear Regression, XGBoost)
   • Evaluate performance and select the best model

4. Prediction Interface

   • Develop a user-friendly web interface using Streamlit
   • Accept real-time inputs (e.g., age, gender, health metrics)
   • Display predicted premium output

5. Deployment

   • Deploy the app to Streamlit Cloud for public accessibility

🛠️ Tech Stack

• Python 3
• Jupyter Notebook
• Pandas, NumPy
• Scikit-learn
• Streamlit

🔗 Live Demo

https://healthcarepremiumpredictionmodel-sid.streamlit.app