optimum eigen value for pca python
pca = PCA().fit(data_rescaled)
% matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (12,6)
fig, ax = plt.subplots()
xi = np.arange(1, 11, step=1)
y = np.cumsum(pca.explained_variance_ratio_)
plt.ylim(0.0,1.1)
plt.plot(xi, y, marker='o', linestyle='--', color='b')
plt.xlabel('Number of Components')
plt.xticks(np.arange(0, 11, step=1)) #change from 0-based array index to 1-based human-readable label
plt.ylabel('Cumulative variance (%)')
plt.title('The number of components needed to explain variance')
plt.axhline(y=0.95, color='r', linestyle='-')
plt.text(0.5, 0.85, '95% cut-off threshold', color = 'red', fontsize=16)
ax.grid(axis='x')
plt.show()