Machine Learning Implementation in Electronic Commerce for Churn Prediction of End User 
Neha Sharma1, Aayush Raj2, Vivek Kesireddy3, Preetham Akunuri4
1Neha Sharma*, Assistant Professor, Department of Information Technology, Manipal University Jaipur (Rajasthan), India.
2Aayush Raj, Department of Information Technology, Manipal University Jaipur (Rajasthan), India.
3Vivek Kesireddy, Department of Information Technology, Manipal University Jaipur (Rajasthan), India.
4Preetham Akunuri, Department of Information Technology, Manipal University Jaipur (Rajasthan), India.
Manuscript received on May 15, 2021. | Revised Manuscript received on May 18, 2021. | Manuscript published on May 30, 2021. | PP: 20-25 | Volume-10 Issue-5, May 2021. | Retrieval Number: 100.1/ijsce.F35020510521 | DOI: 10.35940/ijsce.F3502.0510521
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Client conduct can be addressed from numerous points of view. The client’s conduct is distinctive in various circumstances will give his concept of client conduct. From an overall viewpoint, the conduct of the client, or rather any individual around there, is taken to be irregular. When noticed distinctly, it is regularly seen that the future conduct of an individual can rely upon different variables of the current circumstance just as the conduct in past circumstances. This examination establishes the forecast of client beat, for example regardless of whether the client will end buying from the purchaser or not, which relies upon different components. We have chipped away at two sorts of client information. To start with, that is reliant upon the current elements which don’t influence the past or future buys. Second, a period arrangement information which gives us a thought of how the future buys can be identified with the buys before. Logistic Regression, Random Forest Classifier, Artificial neural organization, and Recurrent Neural Network has been carried out to find the connections of the agitate with different factors and order the client beat productively. The correlation of calculations demonstrates that the aftereffects of Logistic Regression were somewhat better for the principal Dataset. The Recurrent Neural Network model, which was applied to the time-arrangement dataset, additionally gave better outcomes.
Keywords: Churn Prediction, Machine Learning, Telecommunication Churn, E- commerce Churn, Logistic Regression, Random Forest Classifier, Artificial neural network, Recurrent Neural Network.