Introduction
In the previous chapter, we introduced decision trees and random forests and saw how they could be used to improve the quality of predictive modeling of the case study data.
In this chapter, we consider model building to be complete and address all the remaining issues that need attention before delivering the model to the client. The two key elements of this chapter are data imputation and financial analysis.
With data imputation, you will explore several strategies for making educated guesses of the missing values of features of the dataset. This should enable you to make predictions for all samples.
In the financial analysis, you will take the final yet crucial steps of understanding how a model can be used in the real world. Your client will likely appreciate the efforts you made in creating a more accurate model or one with higher ROC AUC. However, they will definitely appreciate understanding how much money the model can help them earn or save and will be happy to receive...
