C3: JUSTIFICATION OF TOOLS: I used python to clean my data because I would like ... I used SciPy to create boxplots and detect normalization of each graph. I used sklearn in order to run a PCA. D1: ...

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To perform PCA in Python, you can use the scikit-learn library ... as they can be shown in a 2D or 3D graph. Performing Principal Component Analysis (PCA) in R requires the use of the prcomp ...

To perform FAMD in Python, you can use the package prince, which also offers other types of factor analysis, such as principal component analysis (PCA), multiple correspondence analysis (MCA), and ...

Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you’ll explore major graph neural ...

To address these issues, we propose a method that combines graph-regularized principal component analysis (graph-regularized PCA) and an ensemble learning framework, random forest, to capture ...