title: python LinearRegression make real time prediction
body: <p>i received data from many captors
  I want to predict electronic failure with linear regression in real time (i want to add new values to my model)</p>
  
  <p>i have small example :</p>
  
  <pre><code>from sklearn.linear_model import LinearRegression
  from sklearn.datasets import make_regression
  #dataset
  #X : Date in millisecond; temperature degree; humidity %
  #y : 0= no problem; 1 = electronic failure
  X=[[969695100000,15,10],[969788280000,30,50],[975042120000,20,3]]
  y=[0,1,0]
  model = LinearRegression()
  model.fit(X, y)
  
  
  # Prediction
  Xnew, _ = make_regression(n_samples=3, n_features=3, noise=0.1, random_state=1)
  ynew = model.predict(Xnew)
  for i in range(len(Xnew)):
      print("X=%s, Predicted=%s" % (Xnew[i], ynew[i]))
  </code></pre>
  
  <p>i have real data X : Date; temperature and humidity
  and y, 0 => no problem, 1 the sensor has failed
  I have new data every days, i want to update my model every day.</p>
  
  <p>my goal is with this data to predict a sensor failure by tomorrow.</p>
  
  <p>my question is: how add data to my model ?</p>