Manuscript received November 27, 2024; revised February 1, 2025; published August 27, 2025
Abstract—Sales forecasting in e-commerce is essential for
optimizing inventory management, enhancing customer
satisfaction, and supporting strategic decision-making. This
study investigates sales forecasting using exploratory data
analysis (EDA) and advanced feature engineering techniques.
Utilizing rich data from a prominent household items retailer in
Saudi Arabia, key patterns and trends influencing sales
performance are identified. Several predictive models are
developed and their performances are evaluated, with a
particular focus on the impact of domain- specific features and
state-of-the-art machine learning algorithms on forecast
accuracy. The findings demonstrate that incorporating
domain-specific features and advanced machine learning
techniques significantly improves sales forecasting precision.
This research provides valuable insights and practical
methodologies for practitioners and researchers aiming to
enhance their e- commerce sales forecasting capabilities. The
implications of the results are discussed, providing a solid
foundation for future work in the field.
Keywords—E-commerce, feature engineering, machine
learning algorithms, sales forecasting
Cite: Roxane Elias Mallouhy, "Forecasting Sales in e-Commerce: Insights from Exploratory Data Analysis and Machine Learning Techniques," International Journal of Machine Learning vol. 15, no. 3, pp. 64-70, 2025.
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).