A comprehensive analysis of Walmart's sales data to uncover factors influencing revenue and explore cost optimization strategies
This project investigates the sales data of one of the largest retailers in the world—Walmart. It delves into key questions:
What factors influence Walmart's revenue? Can external elements such as air temperature, fuel prices, and the unemployment rate significantly affect sales? How can machine learning models be leveraged to optimize costs and maximize the company's economic impact? The findings from this analysis can serve as a foundation for decision-making and strategic planning for retail businesses.
The dataset comprises weekly sales data across various stores, along with factors that could potentially influence sales. The following columns are included:
Store: Unique identifier for the store. Date: The start date of the sales week. Weekly_Sales: Total sales for the week (target variable). Holiday_Flag: Binary flag indicating the presence (1) or absence (0) of a holiday during the week. Temperature: Average air temperature in the store’s region (°F). Fuel_Price: Average cost of fuel in the region. CPI: Consumer Price Index, indicating purchasing power. Unemployment: Regional unemployment rate.
Data Preprocessing: Microsoft Excel Data Analysis & Machine Learning: Python (via Jupyter/VS Code) Libraries: pandas, matplotlib, seaborn, scikit-learn Dashboard Creation: Microsoft Power BI Version Control: GitHub