This project aims to predict the insurance premium for a policyholder based on various factors such as age, gender, smoking habit, etc. The model is trained on a dataset of policyholder information and premium amounts.
The following packages are required to run the code:
- NumPy
- Pandas
- Scikit-learn
- Matplotlib (optional, for visualizing results)
To set up the environment, you need to install the required packages. You can use the following command to install them using pip: pip install numpy pandas scikit-learn matplotlib
The code is written in Python and can be executed using a Jupyter Notebook or any other Python environment.
The dataset used for training and testing the model is included in the data folder. To train the model, simply run the cells in the Jupyter Notebook or execute the script run.py.
insurance_premium_prediction.py: The main script that trains the model and predicts insurance premiums.data: The folder containing the dataset used for training and testing the model.models: The folder to save the trained model (optional).results: The folder to save the results of the model (optional).
The model used in this project is a simple linear regression model. However, you can experiment with other regression algorithms or try to improve the performance of the model by fine-tuning the hyperparameters or using a more complex model.
The performance of the model is evaluated using mean squared error (MSE) and R-squared (R^2) metrics. The results show that the model is able to make accurate predictions with a low MSE and a high R^2 value.
This project provides a basic implementation of an insurance premium prediction model. The code and methodology can be used as a starting point for building more advanced models or for exploring different regression algorithms.
This project is licensed under the MIT License. See the LICENSE file for details.
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