Improving Model Performance of the Prediction of Online Shopping Using Oversampling and Feature Selection

Author:

Ahsain Sara,Ait Kbir M’hamed

Publisher

Springer Nature Switzerland

Reference13 articles.

1. Lecture Notes in Networks and Systems;S Ahsain,2021

2. Ahsain, S., Ait Kbir, M.: Predicting the client’s purchasing intention using Machine Learning models. In: WITS 2022 (2022)

3. Sakar, C.O., Polat, S.O., Katircioglu, M., Kastro, Y.: Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and LSTM recurrent neural networks. Neural Comput. Appl. 31(10), 6893–6908 (2018). https://doi.org/10.1007/s00521-018-3523-0

4. Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/Online+Shoppers+Purchasing+Intention+Dataset. Accessed 19 Apr 2022

5. Oversampling method SMOTE. https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.SMOTE.html. Accessed 19 Apr 2022

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