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ABSTRACT
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. Customer churn refers to the number of customers who have ceased utilizing
the company’s product or service over a period of time.
The number of customers lost within a certain time frame divided by the number of
active customers at the start of the period is one way to compute a churn rate. For
example, if you gained 1000 clients last month but lost 50, the monthly churn rate
would be 5%Every month, the active customer base is fed into a Machine Learning
Predictive Model, which calculates the likelihood of each client churning, will be
sorted from highest to lowest probability value (or score. Clients with a low
likelihood of turnover (or, in other words, customers for whom the model forecasts
no churn) are satisfied customers.
