Predicting E-Commerce Sales with Three Machine Learning Models

Xinyan Li ()
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Xinyan Li: International College Beijing, China Agricultural University

A chapter in Proceedings of the 2024 2nd International Conference on Management Innovation and Economy Development (MIED 2024), 2024, pp 445-454 from Springer

Abstract: Abstract Online shopping has gained popularity with the advent of e-commerce, owing to its convenience, wide range of choices, and reduced geographical limitations. At the same time, competition among e-commerce enterprises has become increasingly fierce, so enhancing the core competitiveness of e-commerce companies is now contingent upon accurately predicting future sales and devising rational sales strategies. Machine learning (ML) techniques play a pivotal role in this process, as they efficiently handle intricate data and reveal underlying patterns within sales figures, thereby enabling precise projections of upcoming trends. By harnessing the power of ML, e-commerce enterprises can gain a competitive edge and stay ahead of the curve in today’s dynamic market. In this paper, sales data on the e-commerce platform of an online retail store registered in the United Kingdom are used to make e-commerce sales predictions employing three distinct ML models: Linear Regression (LR), Decision Tree (DT), and Random Forest (RF). Subsequently, the performance of these models is evaluated by calculating their Mean Absolute Error (MAE), Mean Square Error (MSE), and R-squared values. The selection of the optimal sales prediction model was based on the fitness of the prediction results obtained from each model. By comparing these three regression evaluation metrics, particularly R-squared, the model with the largest R-squared is selected as the one that predicts sales most accurately.

Keywords: Machine Learning Models; Sales Prediction; E-Commerce Sales (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-542-3_52

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