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文章基本信息

  • 标题:A Customer Churn Prediction using Pearson Correlation Function and K Nearest Neighbor Algorithm for Telecommunication Industry
  • 本地全文:下载
  • 作者:Nilam Nur Amir Sjarif ; Muhammad Rusydi Mohd Yusof ; Doris Hooi-Ten Wong
  • 期刊名称:International Journal of Advances in Soft Computing and Its Applications
  • 印刷版ISSN:2074-8523
  • 出版年度:2019
  • 卷号:11
  • 期号:2
  • 页码:46-59
  • 出版社:International Center for Scientific Research and Studies
  • 摘要: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%.
  • 关键词:Customer Churn Prediction; Pearson Correlation; Machine Learning; K Nearest Neighbor.
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