In this chapter, we have seen how to develop a regression model for analyzing insurance severity claims using LR and GLR algorithms. We have also seen how to boost the performance of the GLR model using cross-validation and grid search techniques, which give the best combination of hyperparameters. Finally, we have seen some frequently asked questions so that the similar regression techniques can be applied for solving other real-life problems.
In the next chapter, we will see another supervised learning technique called classification through a real-life problem called analyzing outgoing customers through churn prediction. Several classification algorithms will be used for making the prediction in Scala. Churn prediction is essential for businesses as it helps you detect customers who are likely to cancel a subscription, product, or service, which also minimizes customer...
