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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | E-commerce customer spend prediction through Hyper parameter tuned regression models |
| Authors Name: | Vishwath Krishna Shankar |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_211825
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| Published Paper Id: | IJNRD2401271 |
| Published In: | Volume 9 Issue 1, January-2024 |
| DOI: | |
| Abstract: | 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. |
| Keywords: | Machine Learning, Regression models, Decision Tree, Random Forest, Grid search cross validation, Hyper parameter tuning |
| Cite Article: | "E-commerce customer spend prediction through Hyper parameter tuned regression models", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 1, page no.c556-c562, January-2024, Available :http://www.ijnrd.org/papers/IJNRD2401271.pdf |
| Downloads: | 000118772 |
| 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 |
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Published Paper ID:IJNRD2401271 Registration ID: 211825 Published In: Volume 9 Issue 1, January-2024 DOI (Digital Object Identifier): Page No: c556-c562 Country: Pleasanton, California, United States Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2401271 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2401271 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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