Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning

The UK financial sector increasingly employs machine learning techniques to enhance revenue and understand customer behaviour. In this study, we develop a machine learning workflow for high classification accuracy and improved prediction confidence using a binary classification approach on a public...

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Main Authors: Ahmad Aldelemy, Raed A. Abd-Alhameed
Format: Article
Language:English
Published: middle technical university 2023-06-01
Series:Journal of Techniques
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Online Access:https://journal.mtu.edu.iq/index.php/MTU/article/view/1226
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author Ahmad Aldelemy
Raed A. Abd-Alhameed
author_facet Ahmad Aldelemy
Raed A. Abd-Alhameed
author_sort Ahmad Aldelemy
collection DOAJ
description The UK financial sector increasingly employs machine learning techniques to enhance revenue and understand customer behaviour. In this study, we develop a machine learning workflow for high classification accuracy and improved prediction confidence using a binary classification approach on a publicly available dataset from a Portuguese financial institution as a proof of concept. Our methodology includes data analysis, transformation, training, and testing machine learning classifiers such as Naïve Bayes, Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, Artificial Neural Networks, AdaBoost, and Gradient Descent. We use stratified k-folding (k=5) cross-validation and assemble the top-performing classifiers into a decision-making committee, resulting in over 92% accuracy with two-thirds voting confidence. The workflow is simple, adaptable, and suitable for UK banks, demonstrating the potential for practical implementation and data privacy. Future work will extend our approach to UK banks, reformulate the problem as a multi-class classification, and introduce pre-training automated steps for data analysis and transformation.
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publishDate 2023-06-01
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spelling doaj-art-c6b45fe590ba41a39f56385ec5fea94f2025-01-19T11:01:51Zengmiddle technical universityJournal of Techniques1818-653X2708-83832023-06-015210.51173/jt.v5i2.1226Binary Classification of Customer’s Online Purchasing Behavior Using Machine LearningAhmad Aldelemy 0Raed A. Abd-Alhameed1https://orcid.org/0000-0002-1764-617XUniversity of Bradford, Bradford, BD7 1DP, United KingdomUniversity of Bradford, Bradford, BD7 1DP, United Kingdom The UK financial sector increasingly employs machine learning techniques to enhance revenue and understand customer behaviour. In this study, we develop a machine learning workflow for high classification accuracy and improved prediction confidence using a binary classification approach on a publicly available dataset from a Portuguese financial institution as a proof of concept. Our methodology includes data analysis, transformation, training, and testing machine learning classifiers such as Naïve Bayes, Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, Artificial Neural Networks, AdaBoost, and Gradient Descent. We use stratified k-folding (k=5) cross-validation and assemble the top-performing classifiers into a decision-making committee, resulting in over 92% accuracy with two-thirds voting confidence. The workflow is simple, adaptable, and suitable for UK banks, demonstrating the potential for practical implementation and data privacy. Future work will extend our approach to UK banks, reformulate the problem as a multi-class classification, and introduce pre-training automated steps for data analysis and transformation. https://journal.mtu.edu.iq/index.php/MTU/article/view/1226Machine LearningCustomer BehaviourClassification AlgorithmsBanking IndustryClassifiers
spellingShingle Ahmad Aldelemy
Raed A. Abd-Alhameed
Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning
Journal of Techniques
Machine Learning
Customer Behaviour
Classification Algorithms
Banking Industry
Classifiers
title Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning
title_full Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning
title_fullStr Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning
title_full_unstemmed Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning
title_short Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning
title_sort binary classification of customer s online purchasing behavior using machine learning
topic Machine Learning
Customer Behaviour
Classification Algorithms
Banking Industry
Classifiers
url https://journal.mtu.edu.iq/index.php/MTU/article/view/1226
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