E-commerce customer spend prediction through Hyper parameter tuned regression models

E-commerce customer spend prediction through Hyper parameter tuned regression models

Registration ID: IJNRD_211825

Published ID: IJNRD2401271

DOI: Click Here to Get

Vishwath Krishna Shankar

Machine Learning, Regression models, Decision Tree, Random Forest, Grid search cross validation, Hyper parameter tuning

With the emergence of the internet, the last decade has seen a revolution in online shopping, and the number of users around the world has dramatically increased. During the pandemic, even more have started shopping online. E-commerce growth has been enormous this decade and has paved the way for numerous studies on customer purchase behavior and spending predictions. This proposed study is based on customers spending time on web and mobile applications to predict their annual spending. The study on customer purchase behavior can improve business strategies, as vendors are able to handle stocks according to customer purchase behaviors. This proposed work uses machine learning regression algorithms to predict annual spending; specifically, regression models such as Decision Trees and Random Forest. The models use hyperparameters tuned using the Grid Search Cross Validation (GSCV) technique. Experimental results showed that the hyperparameter-tuned Random Forest model has the highest accuracy in e-commerce customer spending prediction.

Published Paper   E-Certificate

"E-commerce customer spend prediction through Hyper parameter tuned regression models", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 1, page no.c556-c562, January-2024, Available :https://ijnrd.org/papers/IJNRD2401271.pdf

Volume 9 Issue 1, January-2024

Pages : c556-c562

Paper Reg. ID: IJNRD_211825

Published Paper Id: IJNRD2401271

Downloads: 000121124

Research Area: Computer Science & Technology 

Country: Pleasanton, California, United States

Published Paper PDF: https://ijnrd.org/papers/IJNRD2401271.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2401271

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 8 | August 2025

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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

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