text: #importing dependencies
  import numpy as np
  import pandas as pd
  from sklearn import datasets
  from sklearn.datasets import load_boston
  from matplotlib import pyplot as plt
  #check accuracy
  from sklearn.metrics import r2_score
  
  
  #understanding data
  boston=datasets.load_boston()
  
  #access data
  for index,name in enumerate(boston.feature_names):
      print(index,name)
  
  
  #reshaping data
  b=boston.data
  
  x=b[:,5].reshape(-1,1)
  y=boston.target.reshape(-1,1)
  
  
  
  #regression
  from sklearn.linear_model import LinearRegression
  
  #creating a regression model
  reg=LinearRegression()
  
  #fit the model
  reg.fit(x,y)
  
  #predicton
  pred=reg.predict(x)
  
  #plot predicted line
  plt.plot(x,pred,color='blue')
  print('linear acc'+str(r2_score(pred,y)))
  
  #using polynomial regression
  from sklearn.preprocessing import PolynomialFeatures
  #to allow merging...powers of polynomials
  from sklearn.pipeline import make_pipeline
  
  model=make_pipeline(PolynomialFeatures(3), reg)
  model.fit(x,y)
  
  pred=model.predict(x)
  
  #check matplotlib
  plt.scatter(x,y,color='green')
  plt.plot(x,pred,color='red')
  plt.xlabel('no. of rooms')
  plt.ylabel('cost of house')
  plt.show()
  
  
  #pred
  print('polynomial acc'+str(r2_score(pred,y)))
  
  
  
  
  