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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM14056

BIGMART SALES PREDICTION

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM14056

Volume:

6 |

6 |

Jun 2022

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM14056

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

AM

Akansha Mishra

APSS

Asst. Prof. Suchi Sharma

SS

Shiwani Singh

AS

Ananya Srivastava

DT

Devika Tripathi

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Abstract

; Machine Learning is a technology that allows machines to become more accurate in predicting outcomes without being explicitly programmed for it. The basic premise of machine learning is to build models and deploy algorithms that can receive input data and use statistical analysis to predict an output while modifying outputs as the new data becomes available. These models can be used in different areas and trained to match the expectations so that accurate steps can be taken to achieve the organization’s target. In this paper, the case of Big Mart Shopping Centre has been discussed to predict the sales of different types of items and for understanding the effects of different factors on the sales of different items. Taking various features of a dataset collected for Big Mart, and the methodology followed for building a predictive model, results with high levels of accuracy are generated, and these observations can be used to take decisions to improve sales.

KEYWORDS

Machine Learning Sales Prediction Big Mart Voting classifier algorithm Linear Regression.

Cite This Article

Akansha Mishra, Asst. Prof. Suchi Sharma, Shiwani Singh, Ananya Srivastava, Devika Tripathi, "BIGMART SALES PREDICTION," International Journal of Scientific Research in Engineering and Management, vol. 6, no. 6, Jun 2022. https://doi.org/10.55041/IJSREM14056

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Publication Date

4/26/2024

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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