title: How to plot SciKit-Learn linear regression graph
body: <p>I am new to SciKit-Learn and I have been working on a regression problem (king county csv) on kaggle. I have been training a regression model to predict the price of the house and I wanted to plot the graph but I have no idea how to do so. I am using python 3.6. Any advice or suggestion would be greatly appreciated.</p>
  <pre><code>#importing numpy and pandas, seaborn
  
  import numpy as np #linear algebra
  import pandas as pd #datapreprocessing, CSV file I/O
  import seaborn as sns #for plotting graphs
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LinearRegression
  from sklearn.model_selection import KFold
  from sklearn.model_selection import cross_val_score
  import matplotlib.pyplot as plt
  
  data = pd.read_csv('kc_house_data.csv')
  data = data.drop('date',axis=1)
  data = data.drop('id',axis=1)
  
  X = data
  Y = X['price'].values
  X = X.drop('price', axis = 1).values
  
  X_train, X_test, Y_train, Y_test = train_test_split (X, Y, test_size = 0.30, random_state=21)
  
  
  reg = LinearRegression()
  kfold = KFold(n_splits=15, random_state=21)
  cv_results = cross_val_score(reg, X_train, Y_train, cv=kfold, scoring='r2')
  
  print(cv_results)
  
  round(np.mean(cv_results)*100, 2)
  </code></pre>