SALES FORECASTING USING MACHINE LEARNING AND DATA SCIENCE

Abstract

In today’s competitive business environment, In competitive markets, predicting future sales accurately is essential for aligning inventory planning with business strategies. This study focuses on using machine learning to improve such forecasts. This project aims to build a machine learning based model to predict future sales using historical data. The model examines variables like product type, regional sales, previous demand patterns, discounts, holidays, and seasonal behaviours to generate insights that support more strategic business planning.The performance of these models is evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The dataset used is pre-processed and split into training and testing sets to ensure the model’s generalization capabilities.  The results indicate that applying machine learning enhances forecasting precision, offering noticeable improvements over conventional statistical approaches. This solution can help businesses optimize inventory levels, allocate resources efficiently, and increase customer satisfaction by ensuring product availability.