Understanding Online Shoppers’ Purchase Intentions using Data Analytics
Keywords:
Overfitting, voting classifier, minority class predictionAbstract
With the advancement in technology, e-commerce or online shopping has gained popularity in comparison to traditional shopping, which has made it difficult to understand customer intentions. In our research, we plan to construct a real-time prediction machine learning system for the online shopping environment to predict the purchase intentions of prospective buyers through various analytical models. We have classified the users based on their revenue-generating propensity, and have applied alternative models including logistic regression, Support Vector Machine, Ada boost, Voting Classifier to predict their intention to purchase.
References
Online Shoppers PurchasingIntention Dataset Data Set: https://archive.ics.uci.edu/ml/datasets/Online+Shoppers+Purchasing+Intention+Dataset
Md Rayhan Kabir, Faisal Bin Ashraf and Rasif Ajwad ” Analysis of Different Predicting Model for Online Shoppers’ Purchase Intention from Empirical Data” ICCIT, 2019 [3] Moe, Wendy W. ”Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream.”
Journal of consumer psychology 13.1-2 (2003): 29-39.