Authors :
P. Yagneshwar Sai; Lavan Satish Vyas; Sulaxan J
Volume/Issue :
Volume 8 - 2023, Issue 8 - August
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/3hv2p44z
DOI :
https://doi.org/10.5281/zenodo.8256011
Abstract :
In today’s industry, shopping outlets and Big
Mart have adopted an practice of tracking sales data for
every product. This helps them predict customer
demand and effectively manage their inventory. This
paper explores the case of Big Mart focusing on
predicting sales, for types of items and understanding
the factors that influence these sales. By analyzing
features from a dataset collected for Big Mart and
employing modeling techniques such as Xgboost, Linear
Regression, Gradient Boosting, AdaBoost and Random
Forest accurate results are obtained. These findings can
then be utilized to make decisions aimed at improving
sales performance.
Keywords :
Big Mart, XGBoost, AdaBoost, Linear Regression.
In today’s industry, shopping outlets and Big
Mart have adopted an practice of tracking sales data for
every product. This helps them predict customer
demand and effectively manage their inventory. This
paper explores the case of Big Mart focusing on
predicting sales, for types of items and understanding
the factors that influence these sales. By analyzing
features from a dataset collected for Big Mart and
employing modeling techniques such as Xgboost, Linear
Regression, Gradient Boosting, AdaBoost and Random
Forest accurate results are obtained. These findings can
then be utilized to make decisions aimed at improving
sales performance.
Keywords :
Big Mart, XGBoost, AdaBoost, Linear Regression.
