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
  
  #display first 5 columns
  #print(df.iloc[:, :5])
  
  y = df['Price']
  #X = df.iloc[:,:9]
  X = df.drop('Price', axis=1)
  
  X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.3, random_state=6)
  
  corr = X_train.corr(method='pearson')
  print(corr)
  
  
  # --------------
  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)
  #mse = mean_squared_error(y_test, y_pred)
  r2 = r2_score(y_test, y_pred)
  print(r2)
  
  
  # --------------
  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, y_pred)
  print(r2_lasso)
  
  
  # --------------
  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, y_pred)
  
  # Code ends here
  
  
  # --------------
  from sklearn.model_selection import cross_val_score
  
  #Code starts here
  regressor = LinearRegression()
  model = regressor
  score = cross_val_score(model, 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)
  print(r2)
  
  
  