Product Success Classification for E-Commerce Summer Sales: Evidence from the Wish Dataset

Raman Kumar; Vladimir Simic; Rupinder Kaur; Harkirat Kaur; Jasleen Kaur; Kanishka Sharma; Vivek Kumar; Vivek John

doi:10.31181/msa31202650

Authors

Raman Kumar 1) Department of Mechanical and Production Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India Author https://orcid.org/0000-0003-2934-7609
Vladimir Simic 2) University of Belgrade, Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, 11010 Belgrade, Serbia; 3) Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul 02841, Republic of Korea Author https://orcid.org/0000-0001-5709-3744
Rupinder Kaur 4) Department of Information Technology, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India Author https://orcid.org/0000-0002-1346-191X
Harkirat Kaur 4) Department of Information Technology, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India; 5) Department of Computer Science and Engineering, Graphic Era (Deemed to be University), Clement Town, Dehradun 248002, India Author https://orcid.org/0009-0003-7032-5257
Jasleen Kaur 4) Department of Information Technology, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India; 6) University Centre for Research and Development, Chandigarh University, Gharuan Mohali, Punjab, India Author https://orcid.org/0009-0005-7102-4776
Kanishka Sharma 4) Department of Information Technology, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India; 7) Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India Author https://orcid.org/0009-0009-4539-2072
Vivek Kumar 8) Computer Science & Engineering, Noida Institute of Engineering & Technology, Greater Noida, India Author https://orcid.org/0000-0003-3123-0741
Vivek John 9) Department of Mechanical Engineering, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, India Author https://orcid.org/0000-0003-2967-4191

DOI:

https://doi.org/10.31181/msa31202650

Keywords:

E-Commerce, Product Classification, Machine Learning Algorithms, Wish Platform, Random Forest, K-Nearest Neighbors

Abstract

This study addresses the task of accurately classifying products on the Wish platform using machine learning (ML) algorithms. The Wish platform is an online e-commerce platform where sellers and buyers transact. This study aims to evaluate and compare the performance of different ML algorithms for product classification. The main methods employed in this study involved collecting a dataset from the Wish platform, consisting of various product attributes. Four ML algorithms, namely Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbors (KNN), were implemented and trained on the dataset. Performance metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve (AUC) were used to evaluate the algorithms. The results of this study showed that RF achieved the highest accuracy (78.93%) and AUC score among the evaluated ML algorithms. LR and KNN also demonstrated competitive performance, while DT had relatively lower accuracy. Feature importance analysis revealed the most influential feature for all models, providing insights into the key factors contributing to product classification on the Wish platform. These findings have implications for decision-making on e-commerce platforms, enabling more accurate classification and targeted strategies for successful product promotion and inventory management.

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