American Journal of Artificial Intelligence

Research Article | | Peer-Reviewed |

Amazon Marketplace: An Analysis of External Factors and Machine Learning Models - Survey

Received: 21 September 2023    Accepted: 20 October 2023    Published: 29 November 2023
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Abstract

This research endeavors to utilize diverse machine learning algorithms to forecast product prices on the Amazon marketplace. The primary objective of the study is to examine the impact of external factors, such as Google Trends and customer reviews, on future product prices and demand. The research process involves gathering unstructured product information and pricing data from Amazon using APIs and crawlers, followed by preprocessing the data through techniques like tokenization and stopword removal. Machine learning algorithms, including decision trees, support vector regression, and random forests, are employed to predict product prices. The study also explores the challenges associated with web scraping and explores potential applications of web harvesting in e-commerce enterprises. To ensure a comprehensive analysis, the research draws upon relevant literature in the field, encompassing the use of machine learning models for stock price forecasting, time series forecasting, and sentiment analysis. By building upon and leveraging existing methodologies, the study aims to contribute to the understanding of price dynamics within the Amazon marketplace. The significance of this research lies in the growing reliance on e-commerce platforms like Amazon for product purchasing. By investigating the relationship between product prices and various influencing variables, this study can provide valuable insights to both sellers and consumers in the ever-evolving online market. Ultimately, the research seeks to predict product prices on the Amazon marketplace using machine learning algorithms and shed light on the dynamics of e-commerce, benefiting sellers and consumers alike.

DOI 10.11648/j.ajai.20230702.13
Published in American Journal of Artificial Intelligence (Volume 7, Issue 2, December 2023)
Page(s) 52-59
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Machine Learning, Predicting Prices, Amazon Data, Google Trends

References
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[2] Mohamed Zaim Shahre, Sofianita Mutalib and Shuzlina Abdul-Rahman (2021) "PriceCop – Price Monitor and Prediction Using Linear Regression and LSVM-ABC Methods for E-commerce Platform", Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia nformation Engineering and Electronic Business, doi: 10.5815/ijieeb.2021.01.01.
[3] Rola M. Elbakly, Magda M. Madbouly, and Shawkat K. Guirguis (2022) "A Hybrid Approach for Product Price Prediction", European Journal of Engineering and Technology Research doi: http://dx.doi.org/10.24018/ejeng.2022.7.5.2883.
[4] Jagruti Hota 1, Sujata Chakravarty 2, Bijay K. Paikaray 3 and Harshvardhan Bhoyar, (2022)." Stock Market Prediction Using Machine Learning Techniques", Savannah, United States Workshop on Advances in Computation Intelligence, its Concepts & Applications., 2022 May 17-19.
[5] Salvatore Carta, Andrea Medda, Alessio Pili, Diego Reforgiato Recupero and Roberto Saia (2018)"Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data, doi: 10.3390/fi11010005.
[6] Shunrong Shen, Haomiao Jiang and Tongda Zhang,(2021) “Stock Market Forecasting Using Machine Learning Algorithms”, Department of Electrical Engineering Stanford University.
[7] Jaydip Sen, Department of Analytics and Information Technology Praxis Business School Kolkata, INDIA,(2018) “Stock Price Prediction Using Machine Learning and Deep Learning Frameworks" 6th International Conference on Business Analytics and Intelligence (ICBAI) At: Indian Institute of Science, Bangalore, INDIA.
[8] Houda Bakir, Ghassen Chniti, and Hédi Zaher (2018)," E-Commerce Price Forecasting Using LSTM Neural Networks", International Journal of Machine Learning and Computing, doi: 10.18178/ijmlc.2018.8.2.682.
[9] Dr. G Madhusudhan, 2Nitin Gopalakrishna Bhat, 2Sahana Venkatraman Patgar, 2Chandan N A, 2Bharath S V, (2021)"E-COMMERCE PRODUCT PRICE TRACKER", 1Assistant. Professor, 2Student 1,2Computer Science and Engineering 1,2JSS Science and Technology University, Mysuru, India Journal of Emerging Technologies and Innovative Research (JETIR) June, Volume 8, Issue6.
[10] Mahla Nikou1 | Gholamreza Mansourfar1 | Jamshid Bagherzadeh2, (2019) "Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms", 1 Faculty of Economics and Management, Urmia University, Urmia, Iran 2 Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran waileyonlinelipray, 20 September, doi: 10.1002/isaf.1459.
[11] Henrys Kasereka "Importance of web scraping in e-commerce and e-marketing (2020) SSRN Electronic Journal doi: 10.6084/m9.figshare.13611395.v1.
[12] Krystyna Kuźniar, Maciej Zając (2015). Some methods of pre-processing input data for neural networks Institute of Fundamental Technological Research, Polish Academy of Science Computer Assisted Methods in Engineering and Science, 22: 141–151.
[13] Shilpi Kulshrestha, M. L. Saini. (2020). Study for the Prediction of E-Commerce Business Market Growth Using Machine Learning Algorithm. 5th IEEE International Conference on Recent Advances and Innovations in Engineering- ICRAIE 2020 (IEEE Record#51050).
[14] Haishan Gao, Jingqian Li, Zhaoqiang Bai (2017). Sales Prediction Based On Product Titles and Images with Deep Learning Approaches CS230: Deep Learning, Fall 2021, Stanford University.
[15] Naeem Ahmed Mahoto1, Rabia Iftikhar1, Asadullah Shaikh2, Yousef Asiri2, Abdullah Alghamdi2 and Khairan Rajab (2021) Tech Science Press Intelligent Automation & Soft Computing doi: 10.32604/iasc.2021.018944.
[16] Isaac Kofi Nti a, Adib Zaman a, Owusu Nyarko-Boateng a, Adebayo Felix Adekoya b, Frimpong Keyeremeh (2023), A predictive analytics model for crop suitability and productivity with tree-based ensemble learning, Decision Analytics Journal doi.org/10.1016/j.dajour.2023.100311
[17] Junaid Maqbool, preeti Aggarwal, Ravreet Kaur, Ajay Mittal, Ishfaq Ganie (2023), Stock Prediction by Integrating Sentiment Scores of Financial News and MLP-Regressor: A Machine Learning Approach, Procedia Computer Science 31 -1 202.
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  • APA Style

    Hazaa Alsurori, M., Abdo Almorhebi, W. (2023). Amazon Marketplace: An Analysis of External Factors and Machine Learning Models - Survey. American Journal of Artificial Intelligence, 7(2), 52-59. https://doi.org/10.11648/j.ajai.20230702.13

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    ACS Style

    Hazaa Alsurori, M.; Abdo Almorhebi, W. Amazon Marketplace: An Analysis of External Factors and Machine Learning Models - Survey. Am. J. Artif. Intell. 2023, 7(2), 52-59. doi: 10.11648/j.ajai.20230702.13

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    AMA Style

    Hazaa Alsurori M, Abdo Almorhebi W. Amazon Marketplace: An Analysis of External Factors and Machine Learning Models - Survey. Am J Artif Intell. 2023;7(2):52-59. doi: 10.11648/j.ajai.20230702.13

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  • @article{10.11648/j.ajai.20230702.13,
      author = {Muneer Hazaa Alsurori and Waheeb Abdo Almorhebi},
      title = {Amazon Marketplace: An Analysis of External Factors and Machine Learning Models - Survey},
      journal = {American Journal of Artificial Intelligence},
      volume = {7},
      number = {2},
      pages = {52-59},
      doi = {10.11648/j.ajai.20230702.13},
      url = {https://doi.org/10.11648/j.ajai.20230702.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20230702.13},
      abstract = {This research endeavors to utilize diverse machine learning algorithms to forecast product prices on the Amazon marketplace. The primary objective of the study is to examine the impact of external factors, such as Google Trends and customer reviews, on future product prices and demand. The research process involves gathering unstructured product information and pricing data from Amazon using APIs and crawlers, followed by preprocessing the data through techniques like tokenization and stopword removal. Machine learning algorithms, including decision trees, support vector regression, and random forests, are employed to predict product prices. The study also explores the challenges associated with web scraping and explores potential applications of web harvesting in e-commerce enterprises. To ensure a comprehensive analysis, the research draws upon relevant literature in the field, encompassing the use of machine learning models for stock price forecasting, time series forecasting, and sentiment analysis. By building upon and leveraging existing methodologies, the study aims to contribute to the understanding of price dynamics within the Amazon marketplace. The significance of this research lies in the growing reliance on e-commerce platforms like Amazon for product purchasing. By investigating the relationship between product prices and various influencing variables, this study can provide valuable insights to both sellers and consumers in the ever-evolving online market. Ultimately, the research seeks to predict product prices on the Amazon marketplace using machine learning algorithms and shed light on the dynamics of e-commerce, benefiting sellers and consumers alike.
    },
     year = {2023}
    }
    

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    AU  - Muneer Hazaa Alsurori
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    T2  - American Journal of Artificial Intelligence
    JF  - American Journal of Artificial Intelligence
    JO  - American Journal of Artificial Intelligence
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    AB  - This research endeavors to utilize diverse machine learning algorithms to forecast product prices on the Amazon marketplace. The primary objective of the study is to examine the impact of external factors, such as Google Trends and customer reviews, on future product prices and demand. The research process involves gathering unstructured product information and pricing data from Amazon using APIs and crawlers, followed by preprocessing the data through techniques like tokenization and stopword removal. Machine learning algorithms, including decision trees, support vector regression, and random forests, are employed to predict product prices. The study also explores the challenges associated with web scraping and explores potential applications of web harvesting in e-commerce enterprises. To ensure a comprehensive analysis, the research draws upon relevant literature in the field, encompassing the use of machine learning models for stock price forecasting, time series forecasting, and sentiment analysis. By building upon and leveraging existing methodologies, the study aims to contribute to the understanding of price dynamics within the Amazon marketplace. The significance of this research lies in the growing reliance on e-commerce platforms like Amazon for product purchasing. By investigating the relationship between product prices and various influencing variables, this study can provide valuable insights to both sellers and consumers in the ever-evolving online market. Ultimately, the research seeks to predict product prices on the Amazon marketplace using machine learning algorithms and shed light on the dynamics of e-commerce, benefiting sellers and consumers alike.
    
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Author Information
  • Computer Science and Information Technology, Science Faculty, Ibb University, Ibb, Yemen

  • Computer Science and Information Technology, Science Faculty, Ibb University, Ibb, Yemen

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