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

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

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...

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Bibliographic Details
Main Authors: Nilam Nur Amir, Sjarif, Muhammad Rusydi, Mohd Yusof, Doris Hooi, Ten Wong, Suraya, Ya’akob, Roslina, Ibrahim, Mohd Zamri, Osman
Format: Article
Language:English
Published: Soft Computing Research Group, UTM Malaysia 2019
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/25630/1/A%20Customer%20Churn%20Prediction%20using%20Pearson.pdf
http://umpir.ump.edu.my/id/eprint/25630/
http://home.ijasca.com/data/documents/04_Page46-59_A-Customer-Churn-Prediction-using-Pearson.pdf
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Institution: Universiti Malaysia Pahang
Language: English
Description
Summary: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%.

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