text: import pickle
  from argparse import ArgumentParser
  
  import numpy as np
  import pandas as pd
  from sklearn.tree import DecisionTreeRegressor
  
  from functions import encode_labels
  
  if __name__ == "__main__":
      parser = ArgumentParser()
      parser.add_argument(
          "-f",
          "--file",
          nargs="+",
          required=True,
          help="Path to file",
          metavar="FILE",
          dest="filename"
      )
  
      args = parser.parse_args()
  
      df1, df2 = [pd.read_parquet(path=file, engine='pyarrow') for file in args.filename]
  
      df1, df2 = encode_labels(df1, df2)
  
      # save predict data set
      df2.to_parquet(fname=args.filename[1], engine='pyarrow')
  
      features = np.delete(
          arr=df1.columns.values,
          obj=np.where((df1.columns.values == 'turnover') | (df1.columns.values == 'label'))
      )
  
      # values of features
      X = np.array(df1[features].values)
  
      # target values
      y = list(df1['turnover'])
  
      mdl = DecisionTreeRegressor()
  
      mdl.fit(X, y)
  
      s = pickle.dump(mdl, open("model/mdl.obj", "wb"))
  