Response Modelling with K-NN AND XNB
Response modelling is one of the important predictive modelling techniques used to get insights into the responses or behaviour of the events like repeat purchase by customers. In this work, application of a lazy learning method called k-NN and a generative model called xNB are used to evaluate the data set for repeat purchase behaviour for an e-commerce business. The RFM data view on a real-world data set is used for performance evaluation. Different values of k for KNN are evaluated for suitability and robustness. A tree augmented naïve Bayes and the BayesNet classifiers are also explored and compared with simple naïve Bayes as the baseline. The results of the experiment are discussed with additional work planned for the project.
Presented and Published at: Proceedings at 5th International Conference on Business Analytics and Intelligence on December 2017 at IIM Bangalore
Dr Jay B. Simha
Professor and Chief Mentor - AI and CTO, ABIBA Systems
Dr. Shinu Abhi
Professor and Director, REVA Academy for Corporate Excellence