Store Performance Analysis using Walmart Sales Data

In modern business analytics, retail sales forecasting has become an essential component it enables organizations to make informed decisions regarding inventory control, demand planning, and operational efficiency [4]. For improving store performance and predicting future sales trends , the rapid growth of retail data, especially from large chains like Walmart, has created opportunities to apply advanced analytical techniques [9]. Main focus is to identify patterns and enhance forecasting accuracy by analyzing Walmart sales data using machine learning and data-driven approaches [2]. Linear Regression, Decision Trees, Random Forest, and Deep Learning techniques are some predictive models integrated in research predictive models [7]. Dataset containing various influencing factors such as temperature, fuel price, consumer price index, unemployment rate, and holiday indicators, which play a significant role in determining sales performance, these models are applied [5]. By tsking into consideration these features, the main aim of study is to capture both seasonal variations and external economic impacts on retail sales [1]. Understanding the trends, associations, and anomalies in the data set Exploratory Data Analysis (EDA) was performed to understand the sales behavior in different stores at different times [8]. It has been found that holidays and promotions affect sales positively, while environmental variables are also influential in generating demand volatility [3]. Furthermore, certain variables including temperature and CPI, are critical in predicting weekly sales accurately features importance analysis [10].