GitHub - saikrishnaneelam/online-shopper-intention-: we propose a real-time online shopper behavior analysis system consisting of two modules which simultaneously predicts the visitor’s shopping intent and Web site abandonment likelihood. In the first module, we predict the purchasing intention of the visitor using aggregated page view data kept track during the visit along with some session and user information. The extracted features are fed to random forest (RF), support vector machines (SVMs) classifiers as input. We use oversampling and feature selection preprocessing steps to improve the performance and scalability of the classifiers. The results show that MLP that is calculated using resilient back propagation algorithm with weight backtracking produces significantly higher accuracy and F1 Score than RF and SVM. Another finding is that although click stream data obtained from the navigation path followed during the online visit convey important information about the purchasing intention of the visitor, combining them with session information-based features that possess unique information about the purchasing interest improves the success rate of the system. In the second module, using only sequential click stream data, we train a long short-term memory-based recurrent neural network that generates a sigmoid output showing the probability estimate of visitor’s intention to leave the site without finalizing the transaction in a prediction horizon. The modules are used together to determine the visitors which have purchasing intention but are likely to leave the site in the prediction horizon and take actions accordingly to improve the Web site abandonment and purchase conversion rates. Our findings support the feasibility of accurate and scalable purchasing intention prediction for virtual shopping environment using click stream and session information data

saikrishnaneelam / online-shopper-intention- Public

Notifications You must be signed in to change notification settings
Fork 1
Star 0

we propose a real-time online shopper behavior analysis system consisting of two modules which simultaneously predicts the visitor’s shopping intent and Web site abandonment likelihood. In the first module, we predict the purchasing intention of the visitor using aggregated page view data kept track during the visit along with some session and u…

0 stars 1 fork Branches Tags Activity

Star

Notifications

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
Model Building VIEW.ipynb		Model Building VIEW.ipynb
Model Building.ipynb		Model Building.ipynb
base_modeling (1).ipynb		base_modeling (1).ipynb
feature details.txt		feature details.txt
online customer_eda (1).ipynb		online customer_eda (1).ipynb
online customer_eda.ipynb		online customer_eda.ipynb
online_shoppers_intention.csv		online_shoppers_intention.csv