text: import numpy as np
  from NeuralNetwork.mynn import MyNN
  from NeuralNetwork.data_preprocessing import DataPreprocessing
  
  
  if __name__ == '__main__':
      data = DataPreprocessing()
      data.load_data('C:\\Users\\kunal\\Documents\\PythonWork\\CE889_Assignment_1\\ce889_dataCollection.csv')
      x_train, x_test, y_train, y_test = data.preprocessing()
      print('max: ', data.max)
      print('min: ', data.min)
      a = MyNN()
      a.init_network(2,6,2)
      # print('Initialized weights: ', a)
      a.fit(x_train, y_train, 1000)
      a.save_model()
      # a.load_model()
      a.max = data.max
      a.min = data.min
      pred_arr = []
      err_arr = []
      for x, y in zip(x_test[:100], y_test[:100]):
          pred, err = a.feed_forward(x, y, predict=True)
          # pred_arr.append(pred)
          # err_arr.append(err)
          print('Input: ', x)
          print('Actual: ', y)
          print('Pred: ', pred.reshape(-1,))
          print('Denormalized prediction: ', a.denormalize(pred.reshape(-1, )))
          print('Denormalized actual: ', a.denormalize(y))
          print(' ')
      # print('Final weights: ', a)
  