Forecasting Student Clothes Purchases Intention in Bangladesh: A Machine Learning Approach

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%.