Customer Churn Prediction on E-Commerce Data using Stacking Classifier

Awasthi, Shashwat

doi:10.36227/techrxiv.20291694

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…The ensemble learning classifier incorporates metaclassifiers from k-NN, SVM, RF Classifiers, and DT. The Stacking Classifier surpasses other ML methods with 98.2% accuracy, 98.1% Area under receiver operating characteristic curve (AUROC), and 95.0% F-Measure score [18].…”

Section: Literature Surveymentioning

confidence: 95%

Customer churn prediction model in enterprises using machine learning

2024

IJATEE

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The business of any enterprises is highly contingent upon their consumers. Understanding how the customers perform with the enterprises business is crucial one. Since the firm's business profit is based upon the customer satisfaction. To comprehend how the customer relationship with enterprises business can be analysed using the customer churn prediction (CCP). The term customer churn refers to frequency at which the client cease doing the business with the enterprises [1]. *Author for correspondenceThe customer churn can be measured using no product purchased during the time of interval by the consumer. Customer churn is significant issue in any enterprises around the globe.Due to numerous factors contributing to customer churn, enterprises often face adverse effects, including revenue loss and the need to invest significant resources in acquiring new customers. The primary causes of customer churn include: Attracting the wrong customers.  Mismatch between product offerings and customer needs.

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Section: Literature Surveymentioning

confidence: 95%