Predicting Customer Churn Using Logistic Regression

In some following posts, I will explore these other methods, such as Random Forest, Support Vector Modeling, and XGboost, to see if we can improve on this customer churn model! In my previous post, we completed a pretty in-depth walk through of the exploratory data analysis process for a customer churn analysis dataset. Our data, sourced from Kaggle, is centered around customer churn, the rate at which a commercial customer will leave the commercial platform that they are currently a (paying) customer, of a telecommunications company, Telco. Now that the EDA process has been complete, and we have a pretty good sense of what our data tells us before processing, we can move on to building a Logistic Regression classification model which will allow for us to predict whether a customer is at risk to churn from Telco's platform. The complete GitHub repository with notebooks and data walkthrough can be found here.