Summary

In this chapter, you started off by understanding the importance of multiclass classification problems and the different categories of these problems. You learned about one-versus-one and one-versus-all classifiers and how to implement them using the scikit-learn module in Python. Next, you went through various micro- and macro-averages of performance metrics and used them to understand the impact of class imbalance on the model performance. You also learned about various sampling techniques, especially SMOTE, and implemented them using the imblearn library in Python. At the end of the chapter, you used an imbalanced marketing campaign dataset to perform dataset exploration, data transformation, model training, performance evaluation, and dataset balancing using SMOTE.

This book started with the basics of data science and slowly covered the entire end-to-end data science pipeline for a marketing analyst. While working on a problem statement, depending on the need, you will...