title: sklearn LinearRegression, why only one coefficient returned by the model?
body: <p>I'm trying out scikit-learn LinearRegression model on a simple dataset (comes from Andrew NG coursera course, I doesn't really matter, look the plot for reference)</p>
  
  <p>this is my script</p>
  
  <pre><code>import numpy as np
  import matplotlib.pyplot as plt
  from sklearn.linear_model import LinearRegression
  
  dataset = np.loadtxt('../mlclass-ex1-008/mlclass-ex1/ex1data1.txt', delimiter=',')
  X = dataset[:, 0]
  Y = dataset[:, 1]
  
  
  plt.figure()
  plt.ylabel('Profit in $10,000s')
  plt.xlabel('Population of City in 10,000s')
  plt.grid()
  plt.plot(X, Y, 'rx')
  
  model = LinearRegression()
  model.fit(X[:, np.newaxis], Y)
  
  plt.plot(X, model.predict(X[:, np.newaxis]), color='blue', linewidth=3)
  
  print('Coefficients: \n', model.coef_)
  
  plt.show()
  </code></pre>
  
  <p>my question is: 
  I expect to have 2 coefficient for this linear model: the intercept term and the x coefficient, how comes I just get one?</p>
  
  <p><img src="https://i.stack.imgur.com/Z6ckd.png" alt="enter image description here"></p>