PYTHON

pca

#if required,do pca 1st 
#then outliers removal then skewness removal

from sklearn.decomposition import PCA
pca=PCA(n_components=20)
pca.fit(df)
x_pca=pca.transform(df)
print(x_pca.shape)
x_pca20=pd.DataFrame(data=x_pca)
x_pca20

PCA

from sklearn.decomposition import PCA
pca=PCA()
X_red = pca.fit_transform(scale(X))

pca

Principal Component Analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data.