Ensemble Approach on the Online Shopping Churn Prediction

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Online shopping grows along with the growing population. An ensemble approach has been drawn for better shopping churn prediction. The algorithms used are KNN, Stacking, Random forest, XGBoost, and Logistic Regression. An accurate prediction of(90.65%) has been achieved for our ensemble approach as the best result. It refers to the customers who have winded up utilizing persistently on the company's service or product. The number of customers lost within a certain time frame divided by the amount of customers who are active at the begin of the term is one way to compute churn rate. For example, if you gained 1000 clients last month but lost 50, the monthly churn rate would be 5%. Every period of month, the customer base which is active are fed into a Machine Learning best Predictive Model, which calculates the probability of each client churning, will be sorted from highest to lowest probability value (or score). Clients with a low probability of value (or, in other words, customers who has the low threshold value the model forecasts no whisk) are content customers.