Prediction of Product Demand in Retail Markets Using Machine Learning Algorithms

Abstract

Retail stores may face difficulties in managing their stock levels effectively. This may cause stockouts or overstocking. This may lead to financial losses and increased operational costs. Forecasting product demand is essential to ensure product availability. This can help to increase customer satisfaction. This project aims to resolve the problem of product demand forecasting using a machine learning model. This project will develop a web application to predict product demand using the Random Forest Regressor algorithm. This algorithm will help to make accurate predictions. This algorithm works by creating multiple decision trees on random data. It combines the results of all the decision trees to make accurate predictions. This can help to avoid overfitting. The data required to make predictions includes product information, sales from last week, monthly sales, and average sales. The application provides an interface for store managers to enter product information and receive immediate demand estimates, along with dynamic stock alerts for low stock or overstocking. This application helps improve the efficiency of business operations by automating the forecasting process, enabling the business to meet customer demands effectively.