A Robust Model for Churn Prediction using Supervised Machine Learning

Migration of customer to other companies is “Churn”. Customers who abandon services of any company or leave the organization are known as churners. Now a day churn prediction is one of the biggest challenge for organizations/Companies. Reputation/Ranking, finance and growth plans of company is directly affected by churn. This makes research on churn more valuable. In this paper python platform is used to implement two of the classification models of supervised machine learning. As the data used is a labeled data thus this is under the category of supervised domain of machine learning. Confusion matrix is observed to state the conclusion which says that KNN is a better approach to predict customer churn over Logistic Regression. KNN is 2.0% more accurate than Logistic Regression to predict customer churn. Here “accuracy” parameter says that Logistic algorithm is least effective to predict the customer churn as compare to K-Nearest Neighbors.