Complete-analysis-of-online-Shopping-behavior

Goals

1. Build a predictive classification model (ensuring optimal features and classifier).
2. Train the model on data entries corresponding to the months of June-Dec, and test the model on data entries corresponding to Feb-March.
3. Generate user-bahavior clusters based on the purchasing behavior data for the complete dataset.
4. Build a semi-supervised self labelling model to estimate 'Revenue' for the missing records in Oct-Dec (presumably) and then fit your classifier.
5. Test classification performance on Feb-March data set with and without the self-labelled data.

Data source

https://archive.ics.uci.edu/ml/datasets/Online+Shoppers+Purchasing+Intention+Dataset

Data Description

Of the 12,330 sessions in the dataset, 84.5% (10,422) were negative class samples that did not end with shopping, and the rest (1908) were positive class samples ending with shopping.

Relevant papers

Sakar, C.O., Polat, S.O., Katircioglu, M. et al. Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and LSTM recurrent neural networks. Neural Comput & Applic 31, 6893–6908 (2019). https://doi.org/10.1007/s00521-018-3523-0