Comprehensive Review on Sales Prediction Models

This study presents a comprehensive investigation into the performance of multiple machine learning algorithms, emphasizing its fundamental aim to conduct a comparative study of diverse machine learning algorithms. Subsequently, this paper delves into the complexities and essential features, setting the stage for the subsequent analytical exploration. The Rossmann Sales dataset is used as a baseline for analytic comparison. Using Light Gradient Boosting Machine, Elastic net regression, XG Boost, Ransac Regression, Linear Regression, Ridge Regression, Random Forest, Decision Tree, K-Nearest Neighbors, and Gradient Boosting, the research focuses on its complexities and key components. Along with evaluating each model’s performance using metrics like MAE, RMSE, and R-squared, the study also looks at the theoretical underpinnings and practical mechanisms of the algorithms. Informed decision-making in the sales analytics and predictive modeling industries is supported by the results, which shed light on the viability and applicability of different models for forecasting Rossmann store sales.