title: python linear regression predict by date
body: <p>I want to predict a value at a date in the future with simple linear regression, but I can't due to the date format. </p>
  
  <p>This is the dataframe I have: </p>
  
  <pre><code>data_df = 
  date          value
  2016-01-15    1555
  2016-01-16    1678
  2016-01-17    1789
  ...  
  
  y = np.asarray(data_df['value'])
  X = data_df[['date']]
  X_train, X_test, y_train, y_test = train_test_split             
  (X,y,train_size=.7,random_state=42)
  
  model = LinearRegression() #create linear regression object
  model.fit(X_train, y_train) #train model on train data
  model.score(X_train, y_train) #check score
  
  print (‘Coefficient: \n’, model.coef_)
  print (‘Intercept: \n’, model.intercept_) 
  coefs = zip(model.coef_, X.columns)
  model.__dict__
  print "sl = %.1f + " % model.intercept_ + \
       " + ".join("%.1f %s" % coef for coef in coefs) #linear model
  </code></pre>
  
  <p>I tried to convert the date unsuccessfully</p>
  
  <pre><code>data_df['conv_date'] = data_df.date.apply(lambda x: x.toordinal())
  
  data_df['conv_date'] = pd.to_datetime(data_df.date, format="%Y-%M-%D")
  </code></pre>