principal component analysis in r
library(FactoMineR)
library(factoextra)
res.pca = FactoMineR::PCA(X, scale.unit = TRUE, ncp = 5, graph = TRUE)
factoextra::fviz_eig(res.pca, addlabels = TRUE, ylim = c(0, 50))principal component analysis in r
library(FactoMineR)
library(factoextra)
res.pca = FactoMineR::PCA(X, scale.unit = TRUE, ncp = 5, graph = TRUE)
factoextra::fviz_eig(res.pca, addlabels = TRUE, ylim = c(0, 50))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_pca20Copyright © 2021 Codeinu
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