🏥 Insurance Claim Prediction System

This project is a complete end-to-end Machine Learning application that predicts whether an insurance claim will be requested based on specific policy and claim details. It features a robust FastAPI backend for model serving and a user-friendly Streamlit frontend.

🚀 Features

• Machine Learning Pipeline: Trained using XGBoost with optimized preprocessing (StandardScaler & OneHotEncoder).
• Data Validation: Powered by Pydantic for strict input validation.
• RESTful API: FastAPI-based backend with automated documentation (Swagger UI).
• Interactive UI: Streamlit-based web interface for easy predictions.

🛠️ Project Structure

Model with APIs/
├── APIwithModel.py          # FastAPI Backend
├── frontend.py              # Streamlit Web App
├── ModelClasses.py          # Pydantic Data Models & Validation
├── ModelBuilding.ipynb      # ML Research & Training Notebook
├── final_insurance_model.pkl # Trained Model Artifact
└── InsurenceData.csv        # Dataset

⚙️ Installation & Setup

1. Clone the Repository:

   git clone <your-repo-url>
   cd "Model with APIs"

2. Install Dependencies:

   pip install fastapi uvicorn streamlit pandas joblib xgboost scikit-learn requests

🏃 How to Run

1. Start the Backend API

python APIwithModel.py

The API will be available at http://127.0.0.1:8000. You can view the interactive docs at /docs.

2. Start the Frontend App

Open a new terminal and run:

streamlit run frontend.py

The web interface will open automatically in your browser.

🧪 Model Inputs

The system expects the following data points:

• Claim Reason: Travel, Medical, Phone, or Other.
• Data Confidentiality: Low, Medium, High, or Very low.
• Claim Amount: The total value of the claim.
• Premium/Amount Ratio: Ratio of policy premium to the claim amount.

---

Developed as part of the FastAPI Learning Journey.

• Dataset Link: https://www.kaggle.com/datasets/usmanfarid/customer-churn-dataset-for-life-insurance-industry