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)
  #Code starts here
  df.head()
  X=df.drop(['Price'],axis=1)
  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()
  
  
  
  # --------------
  from sklearn.linear_model import LinearRegression
  from sklearn.metrics import r2_score
  import numpy as np
  # 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)
  
  
  