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The study develops a visualization dataset, performs data preprocessing, and applies multiple classification methods including Random Forest, XGBoost, Gradient Descent, Logistic Regression, K-NN, support-vector machines, and Keras. It trains and evaluates models, predicts for four classifiers, defines target populations, and gathers Twitter data. 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Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration.  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 17. Sep. 2026  \nUsing Myers-Briggs Personality Classification to Analyse and Compare Personality Types of Successful Athletes and Entrepreneurs Based on Text  \nCasper Timmermans  \n[c.s.timmermans@student.tue.nl](c.s.timmermans@student.tue.nl)  \nDepartment of Computer Science Eindhoven University of Technology  \nTable of Contents  \n1. Introduction  \n• 1.1 Myers-Briggs Type Indicator  \n▫ 1.1.1. Introversion versus Extroversion (I vs E)  \n▫ 1.1.2 Intuition versus Sensing (N vs S)  \n▫ 1.1.3 Thinking versus Feeling (T vs F)  \n▫ 1.1.4 Judging versus Perception (J vs P)  \n• 1.2 Shapiro-Wilk test  \n• 1.3 T-tests  \n• 1.4 Mann-Whitney U test  \n• 1.5 Kruskal–Wallis one-way analysis of variance  \n2. Method  \n• 2.1 Visualization dataset for model  \n• 2.2 Pre-processing data  \n• 2.3 Methods of classification  \n▫ 2.3.1 Random Forest  \n▫ 2.3.2 XG boost Classifier  \n▫ 2.3.3 Gradient Descent  \n▫ 2.3.4 Logistic Regression  \n▫ 2.3.5 K-NN Classifier  \n▫ 2.3.6 Support-vector machines  \n▫ 2.3.7 Keras  \n• 2.4 Training and evaluating  \n• 2.5 Predicting for four classifiers  \n• 2.6 Defining populations  \n• 2.7 Gathering Twitter data  \n• 2.8 Floats  \n3. Results  \n4. Discussion  \n• 4.1 Shapiro-Wilk test  \n• 4.2 Unpaired t-test  \n• 4.3 Mann-Whitney U Test  \n• 4.4 Kruskal-Wallis H-test  \n• 4.5 Reviewing the hypothesis  \n5. Conclusion  \n6. Limitations and future recommendations  \n7. References  \n8. Appendix  \n1. Introduction  \nThere has been a sneaking suspicion that entrepreneurs as a group have unique personalities for along time (Gartner, 1988) . Withing the last two decades, however, the discussion surrounding the personality in entrepreneurship has re-emerged, denouncing the initial traits that study from abounding perspectives, including, but not limited to, the personality measures not being especially developed for entrepreneurs (Robinson et al., 1991), trying to study too many traits, which resulted in anemic correlations among constructs and making use of an inadequate number of quantitative methods (Baum et al., 2014) .  \nAlongside other factors, such as the environment and human capital like education and experience, personality is another aspect of the complex model of entrepreneurship (Brandstätter 2011; Brändle, Berger, Golla, and Kuckertz 2018) . Entrepreneurs’ personality traits have been studied in various contexts; for example, that of the tasks required of entrepreneurs (Rauch and Frese 2007), and in comparison to managers (Zhao, Seibert, and Lumpkin 2010; Stewart and Roth 2001), and most research conc","cbCaifJp3IfhksCg","https://ap.wps.com/l/cbCaifJp3IfhksCg","pdf",904712,32,"English","# Introduction\n## Myers-Briggs Type Indicator\n## Shapiro-Wilk test\n## T-tests\n## Mann-Whitney U test\n## Kruskal–Wallis one-way analysis of variance\n# Method\n## Visualization dataset for model\n## Pre-processing data\n## Methods of classification\n## Training and evaluating\n## Predicting for four classifiers\n## Defining populations\n## Gathering Twitter data\n## Floats\n# Results\n# Discussion\n## Shapiro-Wilk test\n## Unpaired t-test\n## Mann-Whitney U Test\n## Kruskal-Wallis H-test\n## Reviewing the hypothesis\n# Conclusion\n# Limitations and future recommendations\n# References\n# Appendix","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"The thesis aims to analyze and compare personality types of successful athletes and entrepreneurs using Myers-Briggs personality classification based on text.\"},{\"question\":\"Which classification and modeling methods are used?\",\"answer\":\"The methodology includes Random Forest, XGBoost classifier, Gradient Descent, Logistic Regression, K-NN, support-vector machines, and Keras.\"},{\"question\":\"How are statistical tests applied in the analysis?\",\"answer\":\"The results and discussion include Shapiro-Wilk, unpaired t-tests, Mann-Whitney U tests, and Kruskal-Wallis H-tests, followed by reviewing the hypothesis.\"}]","Using Myers-Briggs Personality Classification to Analyse and Compare Personality Types of Successful Athletes and Entrepreneurs Based on Text - Thesis | PDF",81]