https://github.com/ghufranbarcha/insurance-claim-prediction

Webapp for prediction of suitable insurance claim for each case
https://github.com/ghufranbarcha/insurance-claim-prediction

machine-learning python3 random-forest streamlit

Last synced: about 1 month ago
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Webapp for prediction of suitable insurance claim for each case

• Host: GitHub
• URL: https://github.com/ghufranbarcha/insurance-claim-prediction
• Owner: GhufranBarcha
• Created: 2024-09-11T17:35:35.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-18T10:05:19.000Z (3 months ago)
• Last Synced: 2024-11-21T16:14:32.021Z (about 1 month ago)
• Topics: machine-learning, python3, random-forest, streamlit
• Language: Python
• Homepage: https://insurance-claim-prediction-app.streamlit.app/
• Size: 9.86 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Insurance Claim Prediction

This repository contains a Streamlit web application that predicts insurance claim amounts and claim class based on various input features. The application uses trained machine learning models to make these predictions.

## Features

The app allows users to input details about a person such as:

- **Age**: Numerical input.

- **Weight**: Numerical input.

- **Blood Pressure**: Numerical input.

- **BMI**: Numerical input.

- **Number of Dependents**: Selection box (0 to 5).

- **Sex**: Selection box (Male/Female).

- **Smoker**: Selection box (Yes/No).

- **Diabetes**: Selection box (Yes/No).

- **Regular Exercise**: Selection box (Yes/No).

- **Hereditary Diseases**: Selection box of various diseases.

- **City**: Selection box of cities across the US.

- **Job Title**: Selection box with job categories.

## How It Works

- The user provides input values for the aforementioned features.

- The app then uses two pre-trained models:

  - **RandomForestModel.pkl**: Predicts the insurance claim amount.

  - **RandomForestClassifier.pkl**: Classifies the claim category.

- The predicted results are displayed on the interface after clicking the "Predict" button.

## Models Used

1. **Random Forest Regressor** for predicting the insurance claim amount.

2. **Random Forest Classifier** for predicting the claim class (e.g., whether a claim will be approved or denied).

## Installation

To run the app locally, follow these steps:

1. Clone the repository:

   ```bash

   git clone https://github.com/GhufranBarcha/Insurance-Claim-Prediction.git