International Journal of Advanced Networking and Applications

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Comparative Analysis of Different Machine Learning Algorithms to Predict Online Shoppers’


Affiliations
1 Research Scholar, Department of Computer Science and Engineering, Career Point University, Kota, India
2 Professor, Department of Computer Science and Engineering, Vivekananda Global University, Jaipur, India
 

The trend of online-shopping has gradually increased and this trend is growing with a fast pace in the present scenario. As the trend of online-shopping is growing day by day, the prediction of consumer purchasing behavior and choices is becoming as a topic of curiosity for the researchers and business-organizations. It is very challenging to predict buying behaviour of clients in advance. The discovery of consumer purchase patterns in advance can be proven useful for increasing the growth of businesses and generation of revenue. This proposed research work is an effort to develop a framework that presents some useful insights and predicts consumers’ shopping behaviour by applying effective machine learning techniques.The present research work studies and analyses the various aspects and dimensions of online shopping which may impact the experience of purchasing by examining the considered data-set. Further, the thorough study of different machine-learning classification algorithms was performed to be applied for developing a new and better model for analyzing the online purchase data. Some chosen algorithms were applied on the selected data-set and performance evaluation was done using the performance metrics. The algorithm that performed well in terms of accuracy and other factors were chosen for developing the new model.

Keywords

Online Shopping, Customer Prediction, Future Preferences, Machine Learning, E-Commerce, Recommendation System, Classification.
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  • Comparative Analysis of Different Machine Learning Algorithms to Predict Online Shoppers’

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Authors

Veena Parihar
Research Scholar, Department of Computer Science and Engineering, Career Point University, Kota, India
Surendra Yadav
Professor, Department of Computer Science and Engineering, Vivekananda Global University, Jaipur, India

Abstract


The trend of online-shopping has gradually increased and this trend is growing with a fast pace in the present scenario. As the trend of online-shopping is growing day by day, the prediction of consumer purchasing behavior and choices is becoming as a topic of curiosity for the researchers and business-organizations. It is very challenging to predict buying behaviour of clients in advance. The discovery of consumer purchase patterns in advance can be proven useful for increasing the growth of businesses and generation of revenue. This proposed research work is an effort to develop a framework that presents some useful insights and predicts consumers’ shopping behaviour by applying effective machine learning techniques.The present research work studies and analyses the various aspects and dimensions of online shopping which may impact the experience of purchasing by examining the considered data-set. Further, the thorough study of different machine-learning classification algorithms was performed to be applied for developing a new and better model for analyzing the online purchase data. Some chosen algorithms were applied on the selected data-set and performance evaluation was done using the performance metrics. The algorithm that performed well in terms of accuracy and other factors were chosen for developing the new model.

Keywords


Online Shopping, Customer Prediction, Future Preferences, Machine Learning, E-Commerce, Recommendation System, Classification.

References