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A Customer Churn Prediction using Pearson Correlation Function and K Nearest Neighbor Algorithm for Telecommunication Industry

Nilam Nur Amir, Sjarif and Muhammad Rusydi, Mohd Yusof and Doris Hooi, Ten Wong and Suraya, Ya’akob and Roslina, Ibrahim and Mohd Zamri, Osman (2019) A Customer Churn Prediction using Pearson Correlation Function and K Nearest Neighbor Algorithm for Telecommunication Industry. International Journal of Advances in Soft Computing & Its Applications, 11 (2). pp. 46-59. ISSN 2074-8523

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Abstract

Customer churn in telecommunication industry is actually a serious issue. The Telco company needs to have a churn prediction model to prevent their customer from moving to another telco. Therefore, the objective of this paper is to propose the customer churn prediction using Pearson Correlation and K Nearest Neighbor algorithm. The algorithm is validated via training and testing dataset with the ratio 70:30. Based on experiment, the result shows that the K Nearest Neighbor algorithm performs well compared to the others with the accuracy for training is 80.45% and testing 97.78%.

Item Type: Article
Uncontrolled Keywords: Customer Churn Prediction, Pearson Correlation, Machine Learning, K Nearest Neighbor.
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Noorul Farina Arifin
Date Deposited: 14 Aug 2019 03:52
Last Modified: 14 Aug 2019 03:52
URI: http://umpir.ump.edu.my/id/eprint/25630
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