In this chapter, you have learned the working principles of logistic regression and its step-by-step solving methodology by iteratively removing insignificant and multi-collinear variables to find the best fit by constantly checking AIC and concordance values to determine the best model in a statistical way. Subsequently we looked at machine learning model and random forest being applied to calculate the test accuracy. It was found that, by carefully tuning the hyperparameters of random forest using grid search, we were able to uplift the results by 10 percent in terms of test accuracy from 80 percent from logistic regression to 90 percent from random forest.
In the next chapter, we will be covering complete tree based models such as decision trees, random forest, boosted trees, ensemble of models, and so on to further improve accuracy!