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A customer churn prediction using pearson correlation function and k nearest neighbor algorithm for telecommunication industry

Nilam Nur Amir Sjarif · M. R. M. Yusof · Doris Hooi-Ten Wong · Shamshul Bahar Yaakob · Rabha Waell Ibrahim · Mohd Zamri Osman

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

Customer churn in telecommunication industry is actually a \nserious 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 \nNearest Neighbor algorithm performs well compared to the others with the accuracy for training is 80.45% and testing 97.78%.

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2000Marketing management: analysis. planning. implementation and control 2015A comparison of machine learning techniques for customer churn prediction 2011Social Effects on Customer Retention 2016Customer churn prediction in the telecommunication sector using a rough set approach 2016A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry 2012A Customer Churn Prediction Model in Telecom Industry Using Boosting 2014Improved churn prediction in telecommunication industry using data mining techniques 2013A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services

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2024An analysis on classification models for customer churn prediction 2024Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression

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