text: import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  
  # Importing dataset
  dataset = pd.read_csv('Position_Salaries.csv')
  X = dataset.iloc[:, 1:2].values
  y = dataset.iloc[:, 2].values
  
  # Splitting the dataset into the Training set and test set
  from sklearn.model_selection import train_test_split
  
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
  
  # Fitting Linear Regression to the dataset
  from sklearn.linear_model import LinearRegression
  
  lin_reg = LinearRegression()
  lin_reg.fit(X, y)
  
  # Fitting Polynomial Regression to the dataset
  
  from sklearn.preprocessing import PolynomialFeatures
  
  poly_reg = PolynomialFeatures(degree=4)
  X_poly = poly_reg.fit_transform(X)
  lin_reg_2 = LinearRegression()
  lin_reg_2.fit(X_poly, y)
  
  # Visualising the linear Regression results
  plt.scatter(X, y, color='red')
  plt.plot(X, lin_reg.predict(X), color='blue')
  plt.title("Truth or bluff (Linear Regression)")
  plt.xlabel('Position level')
  plt.ylabel('Salary')
  plt.show()
  # Visualising the Polynomial Regression results
  X_grid = np.arange(min(X), max(X), 0.1)
  X_grid = X_grid.reshape((len(X_grid), 1))
  plt.scatter(X, y, color='red')
  plt.plot(X_grid, lin_reg_2.predict(poly_reg.fit_transform(X_grid)), color='blue')
  plt.title("Truth or bluff (Polynomial Regression)")
  plt.xlabel('Position level')
  plt.ylabel('Salary')
  plt.show()
  
  # Predicting a new result with Linear Regression
  lin_reg.predict(6.5)
  
  # Predicting  anew result with Polynomial Regression
  lin_reg_2.predict(poly_reg.fit_transform(6.5))
  