title: Feature/Variable importance after a PCA analysis
body: <p>I have performed a PCA analysis over my original dataset and from the compressed dataset transformed by the PCA I have also selected the number of PC I want to keep (they explain almost the 94% of the variance). Now I am struggling with the identification of the original features that are important in the reduced dataset. 
  How do I find out which feature is important and which is not among the remaining Principal Components after the dimension reduction?
  Here is my code:</p>
  
  <pre><code>from sklearn.decomposition import PCA
  pca = PCA(n_components=8)
  pca.fit(scaledDataset)
  projection = pca.transform(scaledDataset)
  </code></pre>
  
  <p>Furthermore, I tried also to perform a clustering algorithm on the reduced dataset but surprisingly for me, the score is lower than on the original dataset. How is it possible? </p>