Inventory Management Through Sales Prediction in the Field of Tiles And Marbles E-Commerce

Abstract

The primary aim of this project is to develop a predictive model for forecasting sales in the e-commerce sector, mainly focusing on tiles and marble products. By leveraging historical sales data, product details, and external factors like seasonal trends and advertising impacts, we employ various machine learning algorithms to enhance inventory management. The methodologies include data pre-processing, feature engineering, and evaluating multiple machine-learning models. The performance metrics used to assess the models are Mean Absolute Error (MAE), Mean Squared Error (MSE), and coefficient of determination (R²). Our results indicate significant improvements in prediction accuracy, optimizing stock levels and reducing stock outs. The proposed model utilizes advanced machine learning techniques such as regression models, decision trees, and ensemble methods to predict future sales accurately. This predictive capability is crucial for maintaining optimal inventory levels, minimizing holding costs, and maximizing customer satisfaction.

Additionally, the model incorporates seasonal trends and advertising impacts, critical factors in the e-commerce domain. By accurately forecasting sales, businesses can better plan their inventory, reduce the risk of overstocking or under stocking, and ultimately enhance operational efficiency. This study underscores the importance of data-driven approaches in improving operational efficiency and customer satisfaction in e-commerce. Integrating machine learning in sales prediction represents a significant advancement in e-commerce strategies, providing a robust framework for future applications in various sectors.