Machine Learning Algorithm for an Accurate Prediction of Sales of Monte Carlo Garments manufacture in Ludhiana District

Abstract

Sales forecasting serves as an essential tool for improving decision-making and operational efficiency within the retail and fashion sectors. This research work examines the use of machine learning algorithms for predicting sales by integrating insights from various findings. Each research finding demonstrates the efficacy of machine learning methods, including regression models, neural networks and ensemble techniques, in accurately forecasting demand trends. Utilising a real-world dataset, the research evaluated the demand for clothing based on colour and size choices, uncovering significant consumer behaviour patterns. Findings indicated that traditional colours like black and white consistently lead in sales, while the availability of sizes is crucial for fulfilling consumer expectations. Visual aids, such as bar charts and heatmaps, effectively illustrated demand distribution, and the top ten best-selling items were identified, providing actionable recommendations for inventory management. The results emphasise the significance of incorporating machine learning into sales forecasting to optimise stock levels, minimise waste, and effectively meet customer needs. This work not only consolidates the methods and results of prior research but also underscores the potential of data-driven techniques in guiding retail strategies. Future researchers can investigate the incorporation of additional factors such as seasonal variations and promotional efforts to develop more effective forecasting models with higher levels of prediction accuracy.