Data Analytics
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Item Clustering Analysis in Football(Dublin Business School, 2024-05) Le Maux, Alex; Terri HoareNowadays data is everywhere in sport, there are sports like basketball in the US that are famous to be focus on individual statistics. Football has some tendencies to go in the same direction. Statistics are often used to highlight individual performances more than the collective behaviour of a team. Smith, R. (2022) wrote about the history of how data arrive in football and write this sentence: “Football has always measured success by what you win, but only in the last twenty years have clubs started to think about how you win”. With the number of data extracted from each game, there is a possibility to exploit these data in a better way to find patterns regarding the collective behaviour of the teams.Item 2023 US Bank Failures: Predicting Insolvency using Text Mining(Dublin Business School, 2024-05) Malit, Robenille; Mehran RafieeFailure of banks is one of the significant risks in the financial system that regulators critically need to manage and look after. Using sentiment results of annual reports filed by US commercial banks to the Securities and Exchange Commission (SEC) from fiscal years 2016 to 2020, this paper attempts to create a predictive classification model that would act as a supplement to existing regulatory oversight of bank insolvency risks. Ensemble models (Random Forest, AdaBoost and Light GBM) have been trained which achieved significant results during testing phase (around 60 to 80% accuracy across the models). When back validated to predict the 2023 SVB bank failure using sample data, the best model (Light GBM) returned results with 81% accuracy.Item Interactive analysis and prediction of us stock prices using time series models in R(Dublin Business School, 2020) Kam, Yean Pyng; Hoare, TerryNo abstract provided.Item Supervised binary image classification using machine learning and convolutional neural networks(Dublin Business School, 2020) Scanlan, Seamus; Kaushik, AbhishekMachine 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 struggled to achieve above 60% training accuracy. Though the CNNs tended to overfit, the inclusion of regularisation via dropouts reduced this effect and the optimal deep learning algorithm developed using Convolutional Neural Networks achieved a training accuracy of 96% and a validation accuracy using unlabelled images of 94%. In a straight comparison the optimal CNN model had an AUC of 94% compared to 51% for kNN and 58% for Naive Bayes when tested using unseen data.Item Pharmacy cost visualiation and comparison(Dublin Business School, 2020) Tobin-Smith, Sarah; ODonnell, RoryNo abstract provided.