Machine Learning Enabled Inventory Prediction

Abstract

Effective inventory management is a critical aspect of business operations, ensuring optimal stock levels, minimizing costs, and meeting customer demand. Accurate inventory prediction plays a pivotal role in achieving these objectives. This project explores the application of the XGBoost algorithm, a powerful machine learning technique, for inventory prediction. XGBoost's ability to handle complex nonlinear relationships and its robust performance make it a promising approach for this task. The project aims to develop an inventory prediction system using the XGBoost algorithm, leveraging historical sales data and other relevant factors to forecast future inventory levels. This project demonstrates the potential of machine learning, specifically the XGBoost algorithm, to revolutionize inventory management practices, enabling businesses to achieve greater efficiency and profitability. Keyword : Machine Learning, Non-linear Relations, Cost Minimization, Stock levels, Profitability, Inventory Prediction.