PRODUCT DEMAND AND FORECASTING

Abstract

Accurate demand forecasting plays a vital role in effective inventory management and overall business success in the retail sector. This project presents a machine learningbased approach to predict daily product demand using a synthetic retail dataset containing over 73,000 records. The dataset includes multiple influencing factors such as sales, inventory levels, pricing, promotional activities, weather conditions, and seasonal variations, enabling a comprehensive analysis of demand patterns. The proposed methodology involves systematic data preprocessing to handle inconsistencies, followed by exploratory data analysis (EDA) to uncover trends, correlations, and key factors affecting product demand. A Linear Regression model is developed using engineered features to capture relationships between input variables and demand. The model is evaluated using standard performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²) to ensure reliability and accuracy. The experimental results demonstrate excellent predictive performance, achieving an R² score of over 0.99, indicating that the model effectively captures the underlying patterns in the data. This project highlights the capability of machine learning techniques, combined with proper feature engineering, to provide accurate demand forecasts. The study also outlines the complete project lifecycle, including problem definition, system design, implementation, and evaluation, and discusses potential improvements for future enhancements.