Summary

In this chapter, we learned about binary classification using logistic regression from the perspective of solving a use case. Let's summarize our learnings in this chapter. We were introduced to classification problems and specifically binary classification problems. We also looked at the classification problem from the perspective of predicting term deposit propensity through a business discovery process. In the business discovery process, we identified different business drivers that influence business outcomes.

Intuitions derived from the exploratory analysis were used to create new features from the raw variables. A benchmark logistic regression model was built, and the metrics were analyzed to identify a future course of action, and we iterated on the benchmark model by building a second model by incorporating the feature engineered variables.

Having equipped yourselves to solve binary classification problems, it is time to take the next step forward. In the next chapter, you will deal with multiclass classification, where you will be introduced to different techniques for solving such problems.