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Predicting Customers Behavior on an Online Retail System Using Association and Clustering Machine Learning Algorithms (Comparative Analysis using SAS and R Languages)
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence, Data Science
Abstract
The aim of this research is to adopt Machine Learning and Data Mining tools in predicting Customers behavior on an Online Retail System Using Association and Clustering Machine learning Algorithms (Comparative Analysis using SAS and R Languages). The objective of the research includes analyzing a dataset using Association and Clustering mining tool to predict customers behaviors? with likely products to purchase based on customers purchase records and to facilitate customer?s product decision on an online retail systems. The research was motivated due to the high demand of online services and products, inadequate customer?s product satisfaction and poor product response time and product decision. Two different data mining methodologies were applied differently in the study, association and clustering algorithm while apriori and k-mean modeling tools where adopted. The data was analyzed with R and SAS Enterprise Miner. The experiments are done on UK-based and registered non-store online retail dataset, sourced from UCI machine learning repository. The result after the experiment was able to provide a model that could facilitate customer product decision making and hence provide fast online retail activities to customers and after a comparative analysis on the two different approaches, it was proven that R is one of the best data modeling tools as it is easier to clean or explore a dataset accurately than in SAS.
Keywords
Artificial Intelligence, Machine Learning, Online Retail, Association, Clustering Algorithm, K-Means Modeling, Apriori modeling and Data Mining tools
References
[1] Meenu Sharma (2014) Clustering in Data Mining: A Brief Review;
[2] International Journal of Core Engineering & Management (IJCEM) Volume 1, Issue 5, accessed from file:///C:/Users/IPROSO~1/AppData/Local/Temp/Data_Clustering_Using_Data_Mining_Techni.pdf
[3] Pravarti Jain And Santosh Kr Vishwakarma (2017) A Case Study on Car
[4] Evaluation and Prediction: Comparative Analysis using Data Mining Models, International Journal of Computer Applications (0975 – 8887) Volume 172 – No.9
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[6] Charu C. Aggarwal and Philip S. Yu (2012) Data Mining Techniques for
[7] Associations, Clustering and Classification, IBM T. J. Watson Research Center, Yorktown Heights, NY 10598 accessed from https://link.springer.com/article/10.1057/dbm.2012.17
[8] Aayushi Maheshwari, Garima Kharbanda and Harsh Patel(2015) Association Rules in Data Mining, accessed from Data_mining_for_the_online_retail_industry_A_case_study_of_RFM_model-based_customer_segmentation_using_data_mining
How to cite this paper
@article{1705288,
author = {Ibekwe Abundance Emerie, Oguoma Ikechukwu Stanley, Ezurike Onyewuchi, Victory Chibuike Onumaku, Ebele Precious Okemba; Obialor Collins Chimezie},
title = {Predicting Customers Behavior on an Online Retail System Using Association and Clustering Machine Learning Algorithms (Comparative Analysis using SAS and R Languages)},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {7},
number = {6},
pages = {164-179},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/17052882.pdf},
abstract = {The aim of this research is to adopt Machine Learning and Data Mining tools in predicting Customers behavior on an Online Retail System Using Association and Clustering Machine learning Algorithms (Comparative Analysis using SAS and R Languages). The objective of the research includes analyzing a dataset using Association and Clustering mining tool to predict customers behaviors? with likely products to purchase based on customers purchase records and to facilitate customer?s product decision on an online retail systems. The research was motivated due to the high demand of online services and products, inadequate customer?s product satisfaction and poor product response time and product decision. Two different data mining methodologies were applied differently in the study, association and clustering algorithm while apriori and k-mean modeling tools where adopted. The data was analyzed with R and SAS Enterprise Miner. The experiments are done on UK-based and registered non-store online retail dataset, sourced from UCI machine learning repository. The result after the experiment was able to provide a model that could facilitate customer product decision making and hence provide fast online retail activities to customers and after a comparative analysis on the two different approaches, it was proven that R is one of the best data modeling tools as it is easier to clean or explore a dataset accurately than in SAS.},
keywords = {Artificial Intelligence, Machine Learning, Online Retail, Association, Clustering Algorithm, K-Means Modeling, Apriori modeling and Data Mining tools},
month = {December},
}