Customer Churn Retention Using Machine Learning

The rate at which customers stop using a company’s services in a given time is called the customer churn rate or attrition rate. Therefore, developing a prediction model that predicts the possible churn customers will be creating an early warning to the company to give better services. This research can be divided into two major areas: dealing with a dataset about customer behaviors in a bank and building churn prediction models. using machine learning algorithms. The data pre-processing part includes dataset description and some adjustments on original dataset to make it accessible for analysis, including deleting unimportant feature and adjusting feature names. Then the study apportions the modified dataset into train set and test set with an 80-20 split. Next, the study imports two kinds of machine learning algorithms, random forest classifier and decision tree classifier, to build churn prediction models. In each model, the The study first performs feature selections and visualizes feature importance in bar graphs. Then the study tests each model on testing set and visualizes model performances using confusion matrices and accuracy scores. Results show that both models get most predictions correct while random forest model has a better performance due to its higher accuracy.