This was an assignment for Statistique Mathématique II (Mathematical Statistics 2), a course taught by Prof. Sophie Dabo-Niang at the Université Libre de Bruxelles (ULB). As part of my Master in Statistics´ program, this class developed the core concepts and procedures for multivariate statistical analysis. Starting with the multivariate normal distribution and Wishart's distribution, these were then used to build inference within gaussian models (estimation, confidence zones, testing). Finally, different techniques of multivariate analysis were studied, notably principal component analysis (PCA) and discriminant analysis (DA).
The assignment consisted of three exercises, focusing on theoretical and practical aspects of principal component analysis (PCA). Jupyter Notebook was used for exercise 2, and in the last question of exercise 1.
The final report is presented partially in French and partially in English.
NOTE: To run the code, Mathematical-Statistics-2_-_Python-PCA-assignment.ipynb, DataDepenses.csv and helper.py must be in the same folder. The helper file simply provides functions which automatize the process of placing figures and tables in designated folders, to facilitate the process of writing the LaTeX report.