text: # --------------
  import numpy as np
  import pandas as pd
  from sklearn.model_selection import train_test_split
  
  # path- variable storing file path
  df = pd.read_csv(path)
  X = df[['Rooms', 'Type', 'Method', 'SellerG', 'Distance', 'Postcode',
  'Bathroom', 'Car', 'Landsize', 'BuildingArea', 'YearBuilt',
  'CouncilArea', 'Longtitude', 'Regionname', 'Propertycount']]
  y = df['Price']
  X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=6)
  corr = X_train.corr()
  print(corr)
  #Code starts here
  
  
  # --------------
  from sklearn.linear_model import LinearRegression
  from sklearn.metrics import r2_score
  
  # Code starts here
  
  regressor = LinearRegression()
  regressor.fit(X_train,y_train)
  y_pred = regressor.predict(X_test)
  r2 = r2_score(y_test,y_pred)
  
  
  # --------------
  from sklearn.linear_model import Lasso
  
  # Code starts here
  lasso = Lasso()
  lasso.fit(X_train,y_train)
  lasso_pred = lasso.predict(X_test)
  r2_lasso = r2_score(y_test,lasso_pred)
  
  
  # --------------
  from sklearn.linear_model import Ridge
  
  # Code starts here
  ridge = Ridge()
  ridge.fit(X_train,y_train)
  ridge_pred = ridge.predict(X_test)
  r2_ridge = r2_score(y_test,ridge_pred)
  
  
  # Code ends here
  
  
  # --------------
  from sklearn.model_selection import cross_val_score
  
  #Code starts here
  regressor = LinearRegression()
  score = cross_val_score(regressor,X_train,y_train,cv=10)
  mean_score = score.mean()
  print (mean_score)
  
  
  # --------------
  from sklearn.preprocessing import PolynomialFeatures
  from sklearn.pipeline import make_pipeline
  
  #Code starts here
  model = make_pipeline(PolynomialFeatures(2), LinearRegression())
  model.fit(X_train,y_train)
  y_pred = model.predict(X_test)
  r2_poly = r2_score(y_test,y_pred)
  
  
  