ACP

Packages :

from sklearn.decomposition import PCA
from matplotlib import pyplot as pl
import numpy as np
import pandas as pd

normalizeData = StandardScaler().fit_transform(data) #normaliser les données

pca = PCA(n_components=3)
projection = pca.fit_transform(tst)

acp = pd.DataFrame( projection )
varaince_exp = np.round( pca.explained_variance_ratio_/sum(pca.explained_variance_ratio_)*100, 0)
acp.columns  = [ 'axe' + str(i) + '(' + str(j) + ')'  for i, j in zip(acp.columns.values, varaince_exp)]

acp.index = data.index

sns.scatterplot( x = acp.iloc[:, 0], y = acp.iloc[:, 1])

Description de la ACP

• pca.explained_variance_ratio_ pourcentage de la variance représentée.