text: # Polynomial Regression
  
  # Importing the libraries
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  
  # Importing the 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.cross_validation import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"""
  
  # Feature Scaling
  """from sklearn.preprocessing import StandardScaler
  sc_X = StandardScaler()
  X_train = sc_X.fit_transform(X_train)
  X_test = sc_X.transform(X_test)"""
  
  #Fitting linear regression model
  
  from sklearn.linear_model import LinearRegression
  lin_reg=LinearRegression()
  lin_reg.fit(X,y)
  
  
  #fitting polynomial regressor to the dataset
  
  from sklearn.preprocessing import PolynomialFeatures
  poly_reg=PolynomialFeatures(degree=3)
  X_poly=poly_reg.fit_transform(X) 
  
  lin_reg2=LinearRegression()
  lin_reg2.fit(X_poly,y)
  
  
  #visualizing the linear regressoin results
  
  plt.scatter(X,y,color='red')
  plt.plot(X,lin_reg.predict(X),color='blue')
  plt.title("Truth or Bluff(for linear regression)")
  plt.xlabel("position")
  plt.ylabel("salary")
  plt.show()
  
  
  #visualizing the polynomial regression results
  
  
  plt.scatter(X,y,color='red')
  #plt.plot(X,lin_reg2.predict(X_poly),color='blue') or we can make it suitable for all other type
  plt.plot(X,lin_reg2.predict(poly_reg.fit_transform(X) ),color='blue')
  plt.title("Truth or Bluff(for polynomial regression)")
  plt.xlabel("position")
  plt.ylabel("salary")
  plt.show()
  
  
  #predict the new result with linear regression
  
  
  lin_reg.predict(6.5)
  
  
  #predict the new result with polynomial regression
  
  lin_reg2.predict(poly_reg.fit_transform(6.5))
  
  
  
  