new_contents: import pandas as pd
  from sklearn.feature_selection import SelectKBest, f_regression
  
  def select_kbest_reg(data_frame, target, k=5):
      """
      Selecting K-Best features regression
      :param data_frame: A pandas dataFrame with the training data
      :param target: target variable name in DataFrame
      :param k: desired number of features from the data
      :returns feature_scores: scores for each feature in the data as 
      pandas DataFrame
      """
      feat_selector = SelectKBest(f_regression, k=k)
      _ = feat_selector.fit(data_frame.drop(target, axis=1), data_frame[target])
      
      feat_scores = pd.DataFrame()
      feat_scores["F Score"] = feat_selector.scores_
      feat_scores["P Value"] = feat_selector.pvalues_
      feat_scores["Support"] = feat_selector.get_support()
      feat_scores["Attribute"] = data_frame.drop(target, axis=1).columns
      
      return feat_scores
  
  df = pd.read_csv("NABIL.csv")
  df.drop(df.columns[[0,1,9,13,14]], axis=1, inplace=True)
  #print(df.columns)
  df.drop(df.index[:19],inplace=True)
  df['Tomorrow Price']=df['Closing Price'].shift(-1)
  df=df[:-1]
  #print(df.tail(10))
  
  per_feat = select_kbest_reg(df, 'Tomorrow Price', k=5)
  per_feat = per_feat.sort(["F Score", "P Value"], ascending=[False, False])
  print("\n Feature Score using regression\n")
  print(per_feat)
  
subject: Select features using regression