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The sales volume and revenue numbers for each individual product is now tracked by large supermarket run-centers known as Big Marts, in order to forecast possible domestic consumption and revise inventory control. By exploring the database server of the data warehouse, inconsistencies and broad patterns are frequently found. The statistics may be utilised by businesses like Big Mart to forecasting potential product sales using a variety of methods involving machine learning. In this project, we have used multiple machine learning algorithms like Linear Regression, Ridge regression, Lasso Regrssion, Decision Tree Regression, Random Forest Regression, Support Vector Regressor, Adaboost Regressor, XGBoost Regression to predict the sales of the products in the Big Mart. We observe that among the mentioned algorithms XGBoost Regression works best in predicting the sales volume. Hence we have created a model using XGBoost Regression and fine tuned it to further improve the accuracy.
About the author
Dr K Venkata Naganjaneyulu presently working as a professor of CSE Dept in Lords Institute of Engineering and Technology (an autonomous institution), affiliated to Osmania University, Hyderabad, Telangana State, India. Dr K Venkata Naganjaneyulu worked as a professor of CSE Data Science Dept in StMary's Goup of Institutions, Hyderabad, JNTU, TS.
