BMSP-ML: big mart sales prediction using different machine learning techniques

Abstract

<span lang="EN-US">Variations in sales over time is the main issue faced by many retailers. To overcome this problem, we attempt to predict the sales by comparing the previous sales data of different stores. Firstly, the primary task is to recognize the pattern of the factors that help to predict sales. This study helps us understand the data and predict sales using many machines learning models. This process gets the data and beautifies the data by imputing the missing values and feature engineering. While solving this problem, predicting the monthly sales value is significant in the study. In addition, an essential element is to clear the missing data and perform proper feature engineering to better understand them before applying them. The experimental results show that the random forest predictor has outperformed ridge regression, linear regression, and decision tree models among the four machine learning techniques implemented in this study. The performance of the proposed models has been evaluated using root mean square error (RMSE).</span>

Time-aware forecasting of search volume categories and actual purchase

“…Within this multifaceted landscape, data mining plays a critical role, as a slew of algorithms, such as XGBoost, have proven their mettle in providing precise demand forecasts. Furthermore, the temporal dimension encapsulated within time series data is an essential cog in the machinery of demand and sales forecasting, accelerating product delivery and providing retailers with the foresight required to offer their products to the market at precisely the right time [ 31 ].…”

“…Given the importance of accurate demand volume information, several studies have focused on product demand forecasting, whereby researchers have developed models to predict demand [ 18 , 21 , 22 , 26 ], while others have focused on predicting sales [ 27 , 28 , 29 , 30 ]. Additionally, some studies emphasized the significance of factors influencing sales, such as online reviews and search data [ 31 ] or customer search data with economic variables [ 32 ], as seen in Ref. [ 16 ].…”

Big Mart Sales Data Visualization and Correlation

“…Most of the time, a forecast is built on the understanding of earlier research with a strong emphasis on the conditions and then considers several variables, such as client preferences, culture, the marketplace, and many others. In essence, we can state that our forecast is based on earlier research findings [20].…”