Leveraging Machine Learning for Sales Forecasting for the Next Year

Abstract

"In today's data-driven retail industry, accurate sales forecasting is crucial for informed decision-making in inventory management, marketing and resource allocation. Traditional forecasting methods often struggle to capture the complex dynamic nature of consumer behavior which leads to inefficiencies such as overstocking or missed sales opportunities. This dissertation explores the application of machine learning (ML) techniques to enhance sales forecasting accuracy in the retail sector, using the Walmart Sales Dataset as a case study.

The research investigates various ML models, including Linear Regression, Decision Trees, Random Forests, Gradient Boosting and XGBoost. Data preprocessing techniques such as feature selection, encoding and normalization were applied to improve model performance. The models were trained and evaluated using key performance metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and R-squared values.

Results indicate that machine learning significantly improves forecasting accuracy compared to traditional methods. Gradient Boosting and Linear Regression models demonstrated the lowest MSE and RMSE values which makes them the most effective techniques for sales prediction. Feature importance analysis highlighted that product category was the primary determinant of sales with demographic and regional factors playing a minor role.

The findings confirm that ML-based forecasting provides a more robust, adaptive approach to predicting sales trends in retail environments. This research contributes to the field of business analytics by demonstrating how ML can optimize decision-making and operational efficiency in retail businesses. Future work could explore the integration of external factors such as economic indicators and weather patterns to further enhance predictive accuracy.

By leveraging machine learning for sales forecasting, retailers can achieve greater efficiency and profitability making this research valuable for both academic and industry applications.

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