Customer transaction prediction system

Devendra Prakash Jaiswal, Srishti Kumar, Partha Mukherjee

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


In this data-driven world, every innovation is targeted towards the attainment of a better future where we can sustain ourselves in the easiest and most comfortable of ways. The desire of this utopia has induced numerous inventions which has led us all to this era where Artificial Intelligence has become a very crucial part of our daily lives. One such implementation which we discuss here, pertains to building a customer transaction predictions system using a completely anonymized dataset. We incorporate deep learning methodologies along with a gradient boosting tree-based algorithm which would help us in identifying the best approach for our problem. This implementation aims to enhance human intuition through techniques, which would be capable of helping the institutions in achieving customer satisfaction and in identifying important features from the anonymized dataset.

Original languageEnglish (US)
Pages (from-to)49-56
Number of pages8
JournalProcedia Computer Science
StatePublished - 2020
Event2020 Complex Adaptive Systems Conference, CAS 2019 - Malvern, United States
Duration: Nov 13 2019Nov 15 2019

All Science Journal Classification (ASJC) codes

  • General Computer Science


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