Forecasting Student Clothes Purchases Intention in Bangladesh: A Machine Learning Approach
Md Mijanur Rahman1, Md. Zahid Hasan2, Md Golam Morshed3, Sanjida Karim4, Mamunur Rashid Alex5
1Md. Mijanur Rahman, Assistant Professor, Department of Computer Science and Engineering, Southeast University, Dhaka, Bangladesh.
2Md. Zahid Hasan, U.G Student, Department of Computer Science and Engineering, Southeast University, Dhaka, Bangladesh.
3Md GolamMorshed, U.G Student, Department of Computer Science and Engineering, Southeast University, Dhaka, Bangladesh.
4Sanjida Karim, U.G Student, Department of Computer Science and Engineering, Southeast University, Dhaka, Bangladesh.
5Mamunur Rashid Alex, U.G Student, Department of Computer Science and Engineering, Southeast University, Dhaka, Bangladesh.
Manuscript received on 23 February 2023 | Revised Manuscript received on 06 March 2023 | Manuscript Accepted on 15 March 2023 | Manuscript published on 30 March 2023 | PP: 91-96 | Volume-11 Issue-6, March 2023 | Retrieval Number: 100.1/ijrte.F74950311623 | DOI: 10.35940/ijrte.F7495.0311623
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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: Online shopping provides an excellent opportunity and platform for today’s traditional businesses. Because of the advancement of online purchasing systems, students often prefer online shopping. Thus, students’ involvement in online purchasing has become an important trend. The research aims to determine university students’ purchase intentions toward Bangladeshi clothing brands using several machine learning approaches. An online questionnaire survey was conducted with 1000 university students, and the study goal is to understand their attitudes to online shopping from a different perspective. This paper represents a comparative study of different machine-learning techniques that have been applied to the problem of customer purchasing intention. The experiments were conducted using supervised machine learning techniques like linear regression, logistic regression, and Support Vector Machine (SVM) was also used to predict university students’ purchase intentions. This study found that students’ age, quality of cloth, purchase discount, and price positively impacted student purchase intentions, but the buying risk negatively affected students’ purchase intentions. Linear regression gives the highest accuracy with maximum features, and the accuracy is 89.2%.
Keywords: Bangladeshi brands, linear regression, Logistic regression, Support vector machine (SVM), online shopping, purchase intention, Machine learning.
Scope of the Article: Machine learning