Big Mart Sales Analysis
Vidya Chitre1, Shruti Mahishi2, Sharvari Mhatre3, Shreya Bhagwat4
1Vidya Chitre, Professor, Department of Information Technology, Vidyalankar Institute of Technology, Mumbai (Maharashtra), India.
2Shruti Mahishi*, Final Year Engineering Student, Department of Information Technology, Vidyalankar Institute of Technology, Mumbai (Maharashtra), India.
3Sharvari Mhatre, Final Year Engineering Student, Department of Information Technology, Vidyalankar Institute of Technology, Mumbai (Maharashtra), India.
4Shreya Bhagwat, Final Year Engineering Student, Department of Information Technology, Vidyalankar Institute of Technology, Mumbai (Maharashtra), India.
Manuscript received on March 12, 2022. | Revised Manuscript received on March 21, 2022. | Manuscript published on April 30, 2022. | PP: 8-11 | Volume-11 Issue-5, April 2022. | Retrieval Number: 100.1/ijitee.E98330411522 | DOI: 10.35940/ijitee.C9833.0411522
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the modern era of reaching new lengths of advancement, every company and enterprise are working on their customer demands as well as their inventory management. The models used by them help them predict future demands by understanding the pattern from old sales records. Lately, everyone is abandoning the traditional prediction models for sales forecasting as it takes a prolonged amount of time to get the expected results. Therefore now the retailers keep track of their sales record in the form of a data set, which comprises price tag, outlet types, outlet location, item visibility, item outlet sales etc.
Keywords: Analysis, Big Mart, Data Science, Machine Learning, Prediction, Regression, XG Boost
Scope of the Article: Machine Learning