I stumbled upon this repository while exploring predictive modeling projects, which I later forked. Have you ever wondered how insurance amounts are determined? I found this dataset intriguing and decided to delve into it! I aimed to understand how various features influence our insurance costs.

1. Exploring the dataset
2. Converting categorical values to numerical
3. Plotting heatmap to visualize dependencies of dependent value on independent features
4. Data visualization (plots of feature vs feature)
5. Plotting skewness and kurtosis
6. Data preparation
7. Prediction using Linear Regression
8. Prediction using SVR
9. Prediction using Ridge Regressor
10. Prediction using Random Forest Regressor
11. Performing hyperparameter tuning for the above mentioned models
12. Plotting graphs to compare model performance
13. Preparing model for deployment
14. Deploying model using Flask

The dataset used can be downloaded from Kaggle - Click to Download

This project builds upon the work of Sahil Chachra, whose original repository served as the foundation for this project's development.

This project is licensed under the terms of the MIT license. See the LICENSE file for details.