E-Commerce Sales Analysis and Prediction in UK

Xinyi Wu

2024

Abstract

During recent years, it has been remarkable that the e-Commerce industry has already grown rapidly. It could provide customers with access to a wide range of products that enable them to make purchases from the convenience of their homes. Nevertheless, this has brought a great number of new challenges to businesses, especially regarding sales, which is the most crucial aspect in e-commerce. To obtain insights and engineer features, the study adopted the EDA and RFM models. Subsequently, several predictive models, including Artificial Neural Network, Linear Regression, Decision Trees, and Random Forest, are utilized to forecast sales. In addition, the performance of these models was evaluated and compared on the basis of various metrics. However, there are still certain limitations and future directions. This research contributes to a better understanding of e-commerce sales dynamics in the UK that could provide valuable insights for businesses and researchers in the field, thus improving sales prediction accuracy and decision making.

Paper Citation

in Harvard Style

Wu X. (2024). E-Commerce Sales Analysis and Prediction in UK. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 447-452. DOI: 10.5220/0013268600004568

in Bibtex Style

@conference{ecai24,
author={Xinyi Wu},
title={E-Commerce Sales Analysis and Prediction in UK},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={447-452},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013268600004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - E-Commerce Sales Analysis and Prediction in UK
SN - 978-989-758-726-9
AU - Wu X.
PY - 2024
SP - 447
EP - 452
DO - 10.5220/0013268600004568
PB - SciTePress