Abstract
Online purchase portals have a spectacular opportunity for business expansion. E-commerce portals have data repositories pertaining to online transactions that could be analyzed through data analytics to find valuable insight for further expansion of business as well as targeted marketing. This study has made an attempt for the implementation of data analytics over the shared data set of Turkey-based e-commerce company. Precisely, a comparative analysis of supervised machine learning algorithms has been worked out for predicting customer behavior and products being brought. Their efficiency has been found out and they have been ranked purpose wise. The implementations of algorithms are carried out in Python.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sılahtaroğlu, G., Dönertaşli, H.: Analysis and prediction of Ε-customers’ behavior by mining clickstream data. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 1466–1472. IEEE
Sılahtaroğlu, G.: Predicting gender of online customer using artificial neural networks. Int. J. Adv. Electron. Comput. Sci. 2(10) (2015). ISSN: 2393-2835
Das, T.K.: A customer classification prediction model based on machine learning techniques. In: 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 321–326. IEEE
Altunan, B., Arslan, E.D., Seyis, M., Birer, M., Üney-Yüksektepe, F.: A data mining approach to predict E-commerce customer behaviour. In: The International Symposium for Production Research, pp. 29–43. Springer, Cham
Peker, S., Kocyigit, A., Eren, P.E.: An empirical comparison of customer behavior modeling approaches for shopping list prediction. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). IEEE
Senecal, S., Kalczynski, P.J., Nantel, J.: Consumers’ decision-making process and their online shopping behavior: a clickstream analysis. J. Bus. Res. 58(11), 1599–1608
Vicari, D., Alfó, M.: Model based clustering of customer choice data. Comput. Stat. Data Anal. 71, 3–13
Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier
Géron, A.: Hands-on Machine Learning with Scikit-Learn and Tensor Flow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media, Inc
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hambarde, K. et al. (2020). Data Analytics Implemented over E-commerce Data to Evaluate Performance of Supervised Learning Approaches in Relation to Customer Behavior. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore. https://doi.org/10.1007/978-981-15-0035-0_22
Download citation
DOI: https://doi.org/10.1007/978-981-15-0035-0_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0034-3
Online ISBN: 978-981-15-0035-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)