Retail Demand Forecaster Using Machine Learning

Abstract

In today’s fast-paced retail environment, accurate sales forecasting is vital for optimizing inventory, pricing strategies, and operational efficiency. Traditional forecasting often struggles with large-scale historical data and dynamic market trends, leading to issues like stockouts, overstocking, and revenue loss. This project aims to solve these challenges through a robust, machine learning-based sales prediction system with a user-friendly web application. To ensure accuracy, advanced algorithms like XGBoost are used. These models are trained on large historical datasets and evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared. Data preprocessing techniques like handling missing values, encoding categories, and scaling features enhance model performance.
A web interface, built with Flask, HTML, CSS, and JavaScript, allows users to input store data and get real-time predictions. Designed for ease of use, even by non-technical users, the system’s integration of machine learning and web technologies ensures scalability and practical application in real-world retail environments.