Insurance Claim Predictor

A Machine Learning-Based Insurance Claim Estimation System

This project is a Streamlit-based Insurance Claim Predictor that provides:

• Insurance claim amount prediction
• Interactive analytics dashboard
• Model performance evaluation
• Prediction history tracking
• Data visualization and insights
• Secure user authentication

The system uses Machine Learning, Insurance Datasets, and Interactive Visualizations to estimate insurance claim amounts based on customer demographic and health-related information.

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Features

• Insurance claim amount prediction
• Interactive analytics dashboard
• Model performance evaluation
• Prediction history tracking
• Data visualization using Plotly
• Correlation analysis
• User authentication system
• Local data storage
• Responsive Streamlit interface

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Project Structure

Insurance-Claim-Predictor/
│
├── app.py
├── analysis_model.ipynb
├── insurance_model.pkl
├── insurance_data.csv
├── customer_data.xlsx
├── requirements.txt
├── prediction_history

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Tech Stack

• Python 3.x
• Streamlit
• Pandas
• NumPy
• Scikit-Learn
• Plotly
• Matplotlib
• Seaborn
• OpenPyXL

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Installation & Setup

Clone the Repository

Repository Link: https://github.com/AmitSharma9754/Insurance-Claim-Predictor

Clone using Git: git clone https://github.com/AmitSharma9754/Insurance-Claim-Predictor.git cd Insurance-Claim-Predictor

Install Dependencies

pip install -r requirements.txt

Run the Application

Run the Streamlit application using:

streamlit run app.py

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Features & Modules

Module / Section	Description
Claim Prediction	Predicts insurance claim amount using machine learning
Analytics Dashboard	Interactive charts and insurance insights
Data Visualization	Visual exploration of insurance datasets
Model Evaluation	R² Score, MAE, and RMSE analysis
Prediction History	Tracks previous predictions
User Authentication	Secure login system
About Section	Project information and user guide

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Technologies Used

• Python
• Streamlit
• Pandas
• NumPy
• Scikit-Learn
• Plotly
• Matplotlib
• Seaborn
• OpenPyXL

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How to Use

1. Login using valid credentials.

2. Enter customer information:

• Age
• Gender
• BMI
• Blood Pressure
• Diabetes Status
• Number of Children
• Smoking Status
• Region

3. Click Predict Claim Amount

4. The application will generate:

• Estimated Insurance Claim Amount
• Prediction Summary
• Data Insights

5. Explore:

• Analytics Dashboard
• Model Metrics
• Prediction History

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Machine Learning Model

Algorithm Used

• Random Forest Regressor

Evaluation Metrics

• R² Score
• Mean Absolute Error (MAE)
• Root Mean Squared Error (RMSE)

Feature Engineering

• Age × BMI
• BMI × Blood Pressure
• Age × Smoker

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Screenshots

Home Page

Insurance Claim Prediction

Analytics Dashboard

Model Performance & Metrics

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Disclaimer

This application is created strictly for educational and learning purposes only.

The insurance claim values generated by this system are machine learning predictions and should not be considered official insurance decisions.

For real insurance policies, claims, and financial decisions, users should consult authorized insurance professionals.

The developer is not responsible for any decisions made based on the predictions generated by this application.

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Contribution

You can contribute by:

• Improving model accuracy
• Enhancing the user interface
• Adding new visualizations
• Optimizing performance
• Fixing bugs

Pull requests are welcome.

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Contact

Amit Sharma
📩 Email: Amitsharma97545@gmail.com
🐙 GitHub: https://github.com/AmitSharma9754