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Exploitation of Labor in Qatar: How Nepali Laborers are Victimized in Preparation for the 2022 FIFA Men’s World Cup

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This paper aims to effectively portray the stories of migrant laborers who have fallen victim to a system of powerful and exploitative institutions and governments that provide labor for the FIFA 2022 World Cup in Qatar. The purpose of this case study, therefore, is to both uncover the causes and…

This paper aims to effectively portray the stories of migrant laborers who have fallen victim to a system of powerful and exploitative institutions and governments that provide labor for the FIFA 2022 World Cup in Qatar. The purpose of this case study, therefore, is to both uncover the causes and magnitude of the crisis and to understand the relationship between the victimized laborers and the perpetrators. Through this study, I present the complex dynamics of a mass geopolitical operation that leads to the victimization of Nepali workers. I specifically outline why this issue is complicated and what the proper interventions may be to resolve it.
ContributorsNyaupane, Pratik (Co-author) / Kassing, Jeffrey W. (Thesis director) / Dutta, Uttaran (Committee member) / School of Politics and Global Studies (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05

Optimizing Prediction Accuracy in the English Premier League: A Comparative Analysis of Machine Learning Models for Forecasting Match Outcomes

Description
This study presents a comparative analysis of machine learning models on their ability to determine match outcomes in the English Premier League (EPL), focusing on optimizing prediction accuracy. The research leverages a variety of models, including logistic regression, decision trees, random forests, gradient boosting machines, support vector machines, k-nearest…

This study presents a comparative analysis of machine learning models on their ability to determine match outcomes in the English Premier League (EPL), focusing on optimizing prediction accuracy. The research leverages a variety of models, including logistic regression, decision trees, random forests, gradient boosting machines, support vector machines, k-nearest neighbors, and extreme gradient boosting, to predict the outcomes of soccer matches in the EPL. Utilizing a comprehensive dataset from Kaggle, the study uses the Sport Result Prediction CRISP-DM framework for data preparation and model evaluation, comparing the accuracy, precision, recall, F1-score, ROC-AUC score, and confusion matrices of each model used in the study. The findings reveal that ensemble methods, notably Random Forest and Extreme Gradient Boosting, outperform other models in accuracy, highlighting their potential in sports analytics. This research contributes to the field of sports analytics by demonstrating the effectiveness of machine learning in sports outcome prediction, while also identifying the challenges and complexities inherent in predicting the outcomes of EPL matches. This research not only highlights the significance of ensemble learning techniques in handling sports data complexities but also opens avenues for future exploration into advanced machine learning and deep learning approaches for enhancing predictive accuracy in sports analytics.
ContributorsTashildar, Ninad (Author) / Osburn, Steven (Thesis director) / Simari, Gerardo (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05