Predicting the cost, and hence the severity, of claims in an insurance company is a real-life problem that needs to be solved in an accurate way. In this chapter, we will show you how to develop a predictive model for analyzing insurance severity claims using some of the most widely used regression algorithms.
We will start with simple linear regression (LR) and we will see how to improve the performance using some ensemble techniques, such as gradient boosted tree (GBT) regressors. Then we will look at how to boost the performance with Random Forest regressors. Finally, we will show you how to choose the best model and deploy it for a production-ready environment. Also, we will provide some background studies on machine learning workflow, hyperparameter tuning, and cross-validation.
For the implementation, we will use Spark ML API for faster computation and massive scalability. In a nutshell, we will learn the following topics throughout this end-to-end project:
- Machine learning and learning workflow
- Hyperparameter tuning and cross-validation of ML models
- LR for analyzing insurance severity claims
- Improving performance with gradient boosted regressors
- Boosting the performance with random forest regressors
- Model deployment