EXPLORATION,VISUALISATIONETAPPRENTISSAGE SUPERVISÉ SURLESDONNEESCOVID-19

Abstract

This thesis presents a comprehensive application of data science to a real-world healthcare case: analyzing and modeling clinical data related to COVID-19. Using a dataset of over 5,000 records and 100 variables, we followed the essential stages of a data science project. The process began with thorough data preprocessing, including cleaning, encoding, handling missing values, and validating the dataset. Then, we conducted detailed exploratory data analysis to uncover distributions, relationships, and patterns. The core of the project is supervised learning. We trained and evaluated several classification algorithms (Random Forest, SVM, KNN, AdaBoost), using metrics such as accuracy, F1-score, and ROC-AUC. This comparative analysis allowed us to select the most effective model for predicting SARS-CoV-2 test outcomes. Our work was carried out using modern tools like Python, Google Colab, and libraries such as Scikit-learn, Pandas, and Seaborn. We also benefited from educational content like Machine Learnia’s tutorials to enhance our methodology. In conclusion, this thesis demonstrates the power of data science in healthcare, while also highlighting the technical, ethical, and operational challenges of integrating artificial intelligence into medical decision-making systems.