Predicting the Demand for Fmcg using Machine Learning
Anish Mebal.P1, Hema.S2, Jothika.S.J3, M.Manochitra4

1Anish Mebal.P* , CSE, National Engineering College, Kovilpatti, India.
2Hema.S, CSE, National Engineering College, Kovilpatt, Kovilpatti, India.
3Jothika.S.J, CSE, National Engineering College, Kovilpatti, India.
4Manochitra.M, Asst. Professor, CSE, National Engineering College, Kovilpatti, India.

Manuscript received on February 17, 2021. | Revised Manuscript received on February 16, 2021. | Manuscript published on February 28, 2021. | PP: 169-171 | Volume-10 Issue-3, February 2021. | Retrieval Number: 100.1/ijeat.C22530210321 | DOI: 10.35940/ijeat.C2253.0210321
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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: Now-a-days the more accurate prediction of the demand for fast-moving consumer goods (FMCG) is a competitive factor for both the manufacturers and retailers, especially in the super markets, wholesale manufacturers and fresh food sectors and other consumable industries. This proposed system presents the benefits of Machine Learning in sales forecasting for short shelf-life and highly-perishable products, as it predict the statistical information as a result, improves inventory balancing throughout the chain, improving availability to consumers and increasing profitability. This performance is done with various classification algorithms and comparative study is done with some metrics like accuracy, precision, recall and f-score. So that it helps in finding customer need and to increase the profit of the manufacturers. 
Keywords: FMCG , Train set, Test set, Goods.