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A project to compare the performance of Random Forests against Support Vector Machine using a wine dataset from Kaggle. Metrics such as precision, recall, f1-score, loss and the 95% confidence interval of the true risk were compared.
LaTeX source file for my Computer Science Thesis "Clinical Data Management Processes and Predictive Machine Learning Models Development for Diagnosis and Rehabilitation in the Cardiovascular Domain", which spans over 100 pages. Research was conducted in collaboration with the multinational company Dedalus
Our study focused on using the Big Five personality inventory to predict traits from students' smartphone sensor data collected over 2 months under the Horizon Europe project. Through correlation analyses and machine learning with cross-validation, we showed that predictions are reliable and accurate enough for practical use.
Machine Learning case study including an exploratory data analysis and fitting a Decision Tree, Random Forest, and XGBoost Model. Interactive notebook with outputs and visualizations: https://yaldan.github.io/ml_case_study/
Contact: Maximilian Bachl, Alexander Hartl. Explores defenses against backdoors and poisoning attacks for Intrusion Detection Systems. Code for "EagerNet" is in the "eager" branch.