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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| Format: | Article |
| Language: | English |
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middle technical university
2023-06-01
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| 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 |
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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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| format | Article |
| id | doaj-art-c6b45fe590ba41a39f56385ec5fea94f |
| institution | Kabale University |
| issn | 1818-653X 2708-8383 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | middle technical university |
| record_format | Article |
| series | Journal of Techniques |
| 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 |
| work_keys_str_mv | AT ahmadaldelemy binaryclassificationofcustomersonlinepurchasingbehaviorusingmachinelearning AT raedaabdalhameed binaryclassificationofcustomersonlinepurchasingbehaviorusingmachinelearning |