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Enhancing Customer Churn Prediction using Machine Learning and Deep Learning Approaches with Principal Component Analysis

by Md Saidul Islam, Taofica Amrine, Tahmina Akter, Muhammad Anwarul Azim
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 44
Year of Publication: 2023
Authors: Md Saidul Islam, Taofica Amrine, Tahmina Akter, Muhammad Anwarul Azim
10.5120/ijca2023923255

Md Saidul Islam, Taofica Amrine, Tahmina Akter, Muhammad Anwarul Azim . Enhancing Customer Churn Prediction using Machine Learning and Deep Learning Approaches with Principal Component Analysis. International Journal of Computer Applications. 185, 44 ( Nov 2023), 21-27. DOI=10.5120/ijca2023923255

@article{ 10.5120/ijca2023923255,
author = { Md Saidul Islam, Taofica Amrine, Tahmina Akter, Muhammad Anwarul Azim },
title = { Enhancing Customer Churn Prediction using Machine Learning and Deep Learning Approaches with Principal Component Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2023 },
volume = { 185 },
number = { 44 },
month = { Nov },
year = { 2023 },
issn = { 0975-8887 },
pages = { 21-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number44/32984-2023923255/ },
doi = { 10.5120/ijca2023923255 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:28:37.407285+05:30
%A Md Saidul Islam
%A Taofica Amrine
%A Tahmina Akter
%A Muhammad Anwarul Azim
%T Enhancing Customer Churn Prediction using Machine Learning and Deep Learning Approaches with Principal Component Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 44
%P 21-27
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this research effort, we present a comprehensive approach for predicting customer churn using a combination of traditional Ma- chine Learning and Deep Learning methodologies. The primary focus of this investigation centers on the crucial phase of Data Pre-Processing, involving fundamental tasks such as the handling of missing data, removal of duplicates, and the elimination of outliers. To enhance data quality and representation, techniques such as Data Transformation, Normalization, and Principal Com- ponent Analysis (PCA) have been employed. To tackle class im- balance, the method of Random Over-Sampling has been implemented. The process of Feature Extraction encompasses One- Hot Encoding and PCA, further enhancing data representation. Subsequently, a diverse set of predictive models has been evaluated, including Random Forest (RF), Support Vector Classifier (SVC), Gaussian Naive Bayes (GNB), Decision Tree (DT), XG- Boost (XGB), Logistic Regression (LR), Artificial Neural Net- work (ANN), Convolutional Neural Network (CNN), Long Short- Term Memory (LSTM), and Recurrent Neural Network (RNN). The results indicate that XGBoost surpasses other models, achieving an exceptional accuracy of 98.26%. Furthermore, a hybrid CNN & XGB model demonstrates an impressive accuracy of 97.53%.

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Index Terms

Computer Science
Information Sciences

Keywords

Customer Churn Prediction Principal Com- ponent Analysis Data Pre-Processing XGBoost CNN Customer Retention.

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