Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
These days shopping malls and Big Marts keep track of their sales data of each and every individual item for predicting future demand of the customer and update the inventory management as well. These data stores basically contain a large number of customer data and individual item attributes in a data warehouse. Further, anomalies and frequent patterns are detected by mining the data store from the data warehouse. The resultant data can be used for predicting future sales volume with the help of different machine learning techniques for the retailers like Big Mart. In this project, we propose a predictive model using Linear Regression technique for predicting the sales of a company like Big Mart and found that the model produces better performance as compared to existing models. We are developing this project using Machine Learning and with Python programming language. We are developing the web application for the prediction using flask(python)
Keywords:
Python, Machine Learning, Flask, Linear Regression, Random Forest.
Cite Article:
"Web Application For Big Mart Sales Prediction ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 5, page no.331 - 334, May-2022, Available :http://www.ijrti.org/papers/IJRTI2205055.pdf
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000205112
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator