Optimizing Classification Methods for Online Buyers’ Purchase Intentions in Bangladesh
Ikbal Ahmed1, Md Mahmudul Hoque2, Nayan banik3, Atiqur Rahman4, Mohammad Nur-E-Alam5, Mohammad Aminul Islam6
1Ikbal Ahmed, Department of CSE, CCN University of Science and Technology, 3500 Cumilla, Bangladesh.
2Md Mahmudul Hoque, Department of CSE, CCN University of Science and Technology, 3500 Cumilla, Bangladesh.
3Nayan banik, Department of CSE, Comilla University, 3500 Cumilla, Bangladesh.
4Atiqur Rahman, School of Science Engineering, Chittagong Independent University, Jamal Khan, Bangladesh.
5Mohammad Nur-E-Alam, Institute of Sustainable Energy, Universiti Tenaga Nasional, Jalan IKRAM- UNITEN, 43000 Kajang, Selangor, Malaysia.
6Mohammad Aminul Islam, Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.
Manuscript received on 28 December 2023 | Revised Manuscript received on 07 February 2024 | Manuscript Accepted on 15 March 2024 | Manuscript published on 30 March 2024 | PP: 17-24 | Volume-12 Issue-6, March 2024 | Retrieval Number: 100.1/ijrte.E798712050124 | DOI: 10.35940/ijrte.E7987.12060324
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The classification of online buyers’ purchasing intentions is of paramount importance, especially in the context of the period of the COVID-19/post-COVID-19 pandemic, as it carries significant implications for the business industry. However, effectively managing the diverse ever-changing intentions of individual Internet customers remains a challenging task. This study aims to improve the classification techniques used to classify different sorts of online buyers’ purchasing intents in Bangladesh. A comprehensive analysis of different classification algorithms reveals that the Random Forest algorithm outperformed other methods, achieving exceptional accuracy rates of 99.9% in training and 89.7% in testing. Conversely, the Gaussian Naive Bayes algorithm demonstrated comparatively lower accuracy, with training testing accuracies of 80% and 79%, respectively. This study contributes not only to a better understanding of online buyers’ purchase intentions in Bangladesh but also provides valuable insights into the business industry. Moreover, our work highlights the potential for future investigations in recognizing Bangla numerals throug gestures to enhance the accuracy of categorizing online buyers’ intended purchases. This research serves as a stepping stone for further advancements in classifying and understanding online buyers’ purchase intentions, ultimately fostering more accurate decision-making in the realm of E-commerce in Bangladesh.
Keywords: Machine Learning, Online Purchase Intention, Random Forest, MLP Classifier, Decision Tree Classifier.
Scope of the Article: Classification