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    • Predictive analysis and deployment for credit card fraud detection using machine learning techniques 

      Finnegan, Ciaran (Dublin Business School, 2020)
      In 2016, the total value of fraud for credit cards issues within the SEPA region was €1.8 billion (Whatman, 2019). The company with which I am employed works in the domain of financial crime prevention software, so I chose ...
    • Supervised binary image classification using machine learning and convolutional neural networks 

      Scanlan, Seamus (Dublin Business School, 2020)
      Machine Learning and Deep Learning Algorithms were investigated in terms of their ability to perform a supervised binary image classification task involving the Kaggle Dogs vs Cats dataset. Machine Learning algorithms ...
    • Exploring trends and factors in the world happiness report 

      Moore, Lisa (Dublin Business School, 2020)
      The World Happiness report, first published in 2012, ranks countries on the basis of their self-reported happiness levels. The report is released each year on World Happiness Day March 20th. Its goal is to underpin the ...
    • Anti-money laundering detection and customer segmentation 

      Kuvaeva, Nadezda (Dublin Business School, 2020)
      In this project we going to cover best practices of the anti-money laundering techniques and methods, such a customer segmentation based on suspicious behaviour and fraud detection machine learning algorithms. The goal of ...
    • Stock price prediction using machine learning 

      Mah, Kia Woon (Dublin Business School, 2020)
      No abstract provided.

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    AuthorFinnegan, Ciaran (1)Kuvaeva, Nadezda (1)Mah, Kia Woon (1)Moore, Lisa (1)Scanlan, Seamus (1)Subject
    Machine learning (5)
    Data Visualisation (1)Money Laundering (1)Neural networks (1)R program language (1)... View MoreDate Issued
    2020 (5)

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