The goal of this project is to give people an estimate of how much they need based on their individual health situation. After that, customers can work with any health insurance carrier and its plans and perks while keeping the projected cost from our study in mind. This can assist a person in concentrating on the health side of an insurance policy rather than the ineffective part.
https://
Dataset is provided by Ineuron
To run the code, first clone this repository and navigate to the project directory:
git clone https://github.com/your-username/insurance-premium-prediction.git
Create a virtual environment
conda create -p venv python==3.8 -y
conda activate venv/
To run this project, you will need python packages present in the requirements file
pip install -r requirements.txt
Then, run the app.py file to start the Flask web application:
python app.py
The machine learning model used in this project is a catboost regression model, which was trained using scikit-learn. The model takes in demographic and medical information about an individual and predicts their insurance premium.
After training the model, we achieved an R-squared value of 0.88 (88% accuracy) on the test data, indicating a moderate level of predictive power. We also created visualizations to explore the relationships between different features and insurance premiums.
Contributions to this project are welcome! To contribute, please follow the standard GitHub workflow for pull requests.
If you have any questions or comments about this project, feel free to contact the project maintainer at prajwalgbdr03@gmail.com.
This project is licensed under the MIT License - see the LICENSE file for details.
- Clone the project
- pip install -r requirements.txt
- python app.py Enjoy the project in a local host