Summary

Predicting customer churn is one of the most common use cases in marketing analytics. Churn prediction not only helps marketing teams to better strategize their marketing campaigns, but also helps organizations to focus their resources wisely.

In this chapter, we explored how to use the data science pipeline for any machine learning problem. We also learned the intuition behind using logistic regression and saw how it is different from linear regression.

We looked at the structure of the data by reading it using a pandas DataFrame. We then used data scrubbing techniques such as missing value imputation, renaming columns, and datatype manipulation to prepare our data for data exploration.

We implemented various data visualization techniques, such as univariate, bivariate, and a correlation plot, which enabled us to find useful insights from the data.

Feature selection is another important part of data modeling. We used a tree-based classifier to select important features for our machine...