Churn Prediction: A Comparative Study Using KNN and Decision Trees

HASSONAH, Mohammad A, RODAN, Ali, AL TAMIMI, Abdel-Karim and ALSAKRAN, Jamal (2020). Churn Prediction: A Comparative Study Using KNN and Decision Trees. In: 2019 Sixth HCT Information Technology Trends (ITT). Piscataway, New Jersey, IEEE, 182-186.

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Abstract

Churn prediction represents one of the most important components of Customer Relationship Management (CRM). In the purpose of retaining customers and maintaining their satisfaction, researchers of many fields including business intelligence, marketing and information technology were motivated to investigate the best methods that deliver the best services for customers. Many machine learning algorithms had been implemented in the purpose of optimally predicting the possible churning customers and making the right decisions at the right moments. Researchers had conducted several studies on various types of algorithms and results were found very promising. In this paper, we are conducting a comparison study of the performance towards churn prediction between two of the most powerful machine learning algorithms which are Decision Tree and K-Nearest Neighbor algorithms. Results were quite interesting showing a quite large dissimilarity in many areas between the two algorithms.

Item Type:	Book Section
Additional Information:	2019 Sixth HCT Information Technology Trends (ITT), 20-21 November 2019, Ras Al Khaimah, United Arab Emirates. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Identification Number:	https://doi.org/10.1109/itt48889.2019.9075077
Page Range:	182-186
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	26 Apr 2023 16:34
Last Modified:	11 Oct 2023 15:45
URI:	https://shura.shu.ac.uk/id/eprint/31048

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