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Paper Details
Paper Title
A Forecast for Big Mart Sales Based on Random Forests and Multiple Linear Regression
Authors
Heramb Kadam, Rahul Shevade, Prof. Deven Ketkar, Mr. Sufiyan Rajguru
Abstract
In today’s world, big malls and marts record sales data of individual items for predicting future demand and inventory management. This data stores a large number of attributes of the item as well as the individual customer data together in a data warehouse. This data is mined for detecting frequent patterns as well as anomalies. This data can be used for forecasting future sales volume with the help of random forests and multiple linear regression model.
In today’s world, big malls and marts record sales data of individual items for predicting future demand and inventory management. This data stores a large number of attributes of the item as well as the individual customer data together in a data warehouse. This data is mined for detecting frequent patterns as well as anomalies. This data can be used for forecasting future sales volume with the help of random forests and multiple linear regression model.
In today’s world, big malls and marts record sales data of individual items for predicting future demand and inventory management. This data stores a large number of attributes of the item as well as the individual customer data together in a data warehouse. This data is mined for detecting frequent patterns as well as anomalies. This data can be used for forecasting future sales volume with the help of random forests and multiple linear regression model.
Keywords- Sales Forecasting, Regression, Random Forests, Machine Learning
Publication Details
Unique Identification Number - IJEDR1804010Page Number(s) - 41-42Pubished in - Volume 6 | Issue 4 | October 2018DOI (Digital Object Identifier) - Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
Heramb Kadam, Rahul Shevade, Prof. Deven Ketkar, Mr. Sufiyan Rajguru, "A Forecast for Big Mart Sales Based on Random Forests and Multiple Linear Regression", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.6, Issue 4, pp.41-42, October 2018, Available at :http://www.ijedr.org/papers/IJEDR1804010.pdf
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