text: __author__ = 'anamika.sharaf'
  # Feature Extraction with RFE
  from pandas import read_csv
  from sklearn.feature_selection import RFE
  from sklearn.linear_model import LogisticRegression
  import pdb
  
  # load data
  names = ['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave_points_mean', 'symmetry_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave_points_se', 'symmetry_se', 'fractal_dimension_se', 'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave_points_worst','symmetry_worst', 'fractal_dimension_worst']
  dataframe = read_csv("data.csv", names=names)
  array = dataframe.values
  X = array[1:,0:29]
  Y = array[1:,29]
  
  # feature extraction
  model = LogisticRegression()
  rfe = RFE(model, 2)
  fit = rfe.fit(X, Y)
  print("Num Features: %d") % fit.n_features_
  print("Selected Features: %s") % fit.support_
  print("Feature Ranking: %s") % fit.ranking_
  