Health Insurance Cost Prediction – ML Deployment with FastAPI & Streamlit This project builds and deploys a machine learning model to predict health insurance charges based on user inputs. It demonstrates an end-to-end ML pipeline: training → API serving → web interface. ├── Health_model.ipynb # Model training notebook ├── APP_HIP.py # FastAPI backend ├── front_end.py # Streamlit frontend ├── final_HIP.pkl # Trained model ├── scaler1.pkl # Feature scaler └── README.md
├── Data preprocessing & encoding ├── Feature scaling ├── Regression model training ├── REST API using FastAPI ├── Interactive web UI using Streamlit ├── Complete ML deployment workflow
1.Age 2.BMI 3.Number of children 4.Sex 5.Smoker
User → Streamlit → FastAPI → ML Model → Prediction → Streamlit
├── Python ├── Pandas, NumPy ├── Scikit-Learn ├── FastAPI ├── Streamlit ├── Uvicorn