In this chapter, we looked at how to analyze a dataset using various statistical techniques. After that, we obtained a basic approach and, by using that approach, we developed a model that didn't even achieve the baseline. So, we figured out what had gone wrong in the approach and tried another approach, which solved the issues of our baseline model. Then, we evaluated that approach and optimized the hyper parameters using cross-validation and ensemble techniques in order to achieve the best possible outcome for this application. Finally, we found out the best possible approach, which gave us state-of-the-art results. You can find all of the code for this on GitHub at https://github.com/jalajthanaki/credit-risk-modelling. You can find all the installation related information at https://github.com/jalajthanaki/credit-risk-modelling/blob/master/README.md.

In the next chapter, we will look at another very interesting application of the analytics domain: predicting the stock price of a given share. Doesn't that sound interesting? We will also use some modern machine learning (ML) and deep learning (DL) approaches in order to develop stock price prediction application, so get ready for that as well!