Fake Cryptocurrency Detection Using Python


Year : 2025 | Volume : 12 | Issue : 01 | Page : 1-7
    By

    Kazi Kutubuddin Sayyad Liyakat,

  1. Professor & Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India

Abstract

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This study investigates the use of Python-based techniques for detecting fraudulent cryptocurrencies, addressing a growing concern in the digital financial ecosystem. The research methodology integrates various data science approaches, including web scraping, API integration, and advanced data analysis using Pandas and NLTK. Machine learning models, particularly classification algorithms such as Random Forest, are employed to analyze key features extracted from cryptocurrency whitepapers, social media discussions, and transactional data. By training these models on relevant datasets, the study aims to differentiate between legitimate and fraudulent cryptocurrencies effectively. The results highlight the potential of these techniques in identifying scams while also revealing challenges such as data scarcity, the dynamic nature of fraudulent schemes, and the need for continuous updates to detection models. To enhance accuracy, the study suggests incorporating network analysis and anomaly detection algorithms. Furthermore, it underscores the importance of collaboration, data sharing, and regulatory support in developing more robust and reliable fraud detection systems to protect investors in the cryptocurrency market.

Keywords: Fake currency, cryptocurrency, python, machine learning, random forest

[This article belongs to Recent Trends in Programming languages (rtpl)]

How to cite this article:
Kazi Kutubuddin Sayyad Liyakat. Fake Cryptocurrency Detection Using Python. Recent Trends in Programming languages. 2025; 12(01):1-7.
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Kazi Kutubuddin Sayyad Liyakat. Fake Cryptocurrency Detection Using Python. Recent Trends in Programming languages. 2025; 12(01):1-7. Available from: https://journals.stmjournals.com/rtpl/article=2025/view=0


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Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 07/02/2025
Accepted 10/02/2025
Published 21/02/2025
Publication Time 14 Days
101

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