code: # Importing the libraries
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  import pickle
  
  dataset = pd.read_csv('hiring.csv')
  
  dataset['experience'].fillna(0, inplace=True)
  
  dataset['test_score'].fillna(dataset['test_score'].mean(), inplace=True)
  
  X = dataset.iloc[:, :3]
  
  # Converting words to integer values
  def convert_to_int(word):
      word_dict = {'one':1, 'two':2, 'three':3, 'four':4, 'five':5, 'six':6, 'seven':7, 'eight':8,
                  'nine':9, 'ten':10, 'eleven':11, 'twelve':12, 'zero':0, 0: 0}
      return word_dict[word]
  
  X['experience'] = X['experience'].apply(lambda x : convert_to_int(x))
  
  y = dataset.iloc[:, -1]
  
  #Splitting Training and Test Set
  from sklearn.linear_model import LinearRegression
  regressor = LinearRegression()
  
  #Fitting model with trainig data
  regressor.fit(X, y)
  
  # Saving model to disk
  pickle.dump(regressor, open('model.pkl','wb'))
  
  # Loading model to compare the results
  model = pickle.load(open('model.pkl','rb'))
  print(model.predict([[2, 9, 6]]))
  
apis: pandas.read_csv sklearn.linear_model.LinearRegression