Leveraging Deep Learning to Forecast E-Commerce Product Fulfillment

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 5

Year of Publication : 2025

Authors : Revelle Akshara, S Kranthi Reddy, Ajay Jain

10.14445/23488549/IJECE-V12I5P110

How to Cite?

Revelle Akshara, S Kranthi Reddy, Ajay Jain, "Leveraging Deep Learning to Forecast E-Commerce Product Fulfillment," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 5, pp. 118-125, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I5P110

Abstract:

In today's rapidly growing e-commerce market, many businesses face a major challenge: understanding a customer's behaviour and predicting its fulfilment based on various attributes. To address this challenge, we propose an approach using deep learning techniques. By leveraging advanced neural network architectures such as Artificial Neural Networks (ANN), Multi Layer Perceptrons (MLP), and Deep Neural Networks (DNN), we aim to uncover complex patterns and relationships within the dataset that traditional methods might overlook. This approach involves data collection, preprocessing data, correlation analysis, implementing and fine-tuning deep learning models, and assessing their performance using evaluating metrics. Among the implemented models, the DNN outperformed others, achieving 72.2% accuracy, 78.4% precision, 77.3% recall, and a 77.8% F1 score. The results demonstrate the DNN efficiency and accuracy of enhancing customer satisfaction in the e-commerce sector, ultimately fulfilling the products and driving sales growth.

Keywords:

Correlation analysis, Deep learning algorithms, E-Commerce, Performance metrics, Preprocessing.

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