Predicting sales of the E-commerce industry with Brazilian dataset using Deep learning algorithms

Gupta, Harsh Vijay (2024) Predicting sales of the E-commerce industry with Brazilian dataset using Deep learning algorithms. Masters thesis, Dublin, National College of Ireland.

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Abstract

Sales prediction is an important aspect for the e-commerce industry that involves inventory management, demand forecasting, and strategic planning. There are various challenges encountered by old traditional methods when dealing with dynamic, unstructured, and complex datasets, which affect the prediction accuracy. The deep learning models like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), help in identifying long-term patterns and sequential dependencies. The objective of this research is to propose a novel hybrid model, a combination of the LSTM model and the GRU model, respectively, to improve the prediction of sales in e-commerce, also capturing and ensuring the balance between long-term dependencies and lower computational efficiency. The study focused on the Brazilian e-Commerce data set, using various data preprocessing techniques, feature engineering, and a strong model evaluation to obtain accurate predictions. The results of Hybrid model was superior, when compared to LSTM and GRU architectures. The model achieved better R² value of 0.91 and even, the lower error metrics of MAE, MSE, RMSE values. The insights derived shows, Hybrid model’s ability of capturing both short-term fluctuations and long-term patterns. This makes model more accurate for prediction of sales. This research contributes widely in the field of e-commerce industry and predictive modeling.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Basilio, Jorge UNSPECIFIED
Uncontrolled Keywords:	Deep learning; e-commerce; LSTM; GRU; Hybrid model
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HF Commerce > Electronic Commerce Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	02 Sep 2025 12:12
Last Modified:	02 Sep 2025 12:12
URI:	https://norma.ncirl.ie/id/eprint/8705

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