Retail Sales Prediction

Accurate retail sales forecasting is critical for optimizing inventory management, reducing operational costs, and improving strategic decision-making. Traditional statistical models often fail to capture complex consumer behaviors and market fluctuations, leading to inaccurate predictions. With advancements in machine learning (ML), predictive models now offer enhanced accuracy and adaptability in forecasting sales trends. This study evaluates the effectiveness of machine learning algorithms, specifically k-Nearest Neighbor (kNN) regression, Multinomial regression, and Decision Tree with AdaBoost regression, in predicting retail sales. The research assesses model performance using standard evaluation metrics, highlighting the advantages and limitations of each approach. The findings provide valuable insights for retailers, business strategists, and policymakers, offering a data-driven framework for improving sales forecasting and decision-making processes.