Insurance Charge Prediction Platform

Welcome to the Insurance Charge Prediction Platform repository! This project is designed to help users forecast their insurance charges using advanced data analytics and machine learning techniques.

Table of Contents

Introduction, Features, Technologies, Installation, Usage, Contributing, Acknowledgements, License

Introduction

The Insurance Charge Prediction Platform provides users with personalized predictions of insurance charges. By analyzing various factors, our platform offers actionable insights to help users understand and manage their insurance expenses more effectively.

Features

Advanced Data Analytics: Utilizes sophisticated algorithms to analyze factors influencing insurance charges. Personalized Predictions: Provides customized forecasts for individuals and businesses. User-Friendly Interface: An intuitive platform for easy exploration and decision-making.

Technologies

Frontend: HTML, CSS, JavaScript, Backend: Flask, Machine Learning: Predictive models for accurate charge estimation

Installation

To get started with the project, follow these steps:

Clone the repository:

git clone https://github.com/RiturajS12/Insurance_charge_prediction.git
cd insurance-charge-prediction

Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate   # On Windows, use `venv\Scripts\activate`

Install the required dependencies:

pip install -r requirements.txt

Run the Flask application:

flask run
Open your browser and navigate to http://127.0.0.1:5000 to explore the platform.

Usage

Navigate to the home page to explore the platform. Use the form to input various factors influencing insurance charges. Get personalized predictions and insights on insurance charges.

Contributing

We welcome contributions to improve the platform! To contribute:

Fork the repository.

Create a new branch:

git checkout -b feature/your-feature-name

Make your changes and commit them:

git commit -m 'Add some feature'

Push to the branch:

git push origin feature/your-feature-name

Create a new Pull Request.