title: Sklearn LinearRegression shapes not alligned
body: <p>I'm trying to use sklearn for a linear regression prediction.</p>
  
  <p>I have two lists of numbers(age in seconds sinds 1970) and some price. </p>
  
  <p>When I try to predict my line I get the following error:</p>
  
  <p>ValueError: shapes (1,1000) and (14697,14697) not aligned: 1000 (dim 1) != 14697 (dim 0)</p>
  
  <p>My code:</p>
  
  <pre><code>inkoop = np.asarray(list(df['Bedrag_Inkoop_Ingediend_Mono']),dtype=np.number)
  tempT = list(df['dtGeboorteDatum'])
  
  dtAge = np.asarray([(time-datetime.date(1970,1,1)).total_seconds() for time in tempT],dtype=np.number)
  
  dtAgeTestY = dtAge[-1000:]
  dtAgeTrainY = dtAge[:-1000]
  inkoopTestX = inkoop[-1000:]
  inkoopTrainX = inkoop[:-1000]
  regr = linear_model.LinearRegression()
  regr.fit(inkoopTrainX,dtAgeTrainY.reshape(1,-1))
  agePredictY = regr.predict(inkoopTestX.reshape(1,-1))
  
  # The coefficients
  print('Coefficients: \n', regr.coef_)
  
  # The mean squared error
  print("Mean squared error: %.2f"
        % mean_squared_error(inkoopTest, agePredictY))
  
  # Explained variance score: 1 is perfect prediction
  print('Variance score: %.2f' % r2_score(inkoopTest, agePredictY))
  
  plt.scatter(inkoopTestX, dtAgeTestY,  color='black')
  plt.plot(dtAgeTestY, agePredictY, color='blue', linewidth=3)
  
  plt.xticks(())
  plt.yticks(())
  
  plt.show()
  </code></pre>
  
  <p>He errors on agePredictY = regr.predict(inkoopTestX.reshape(1,-1)). I also tried without reshaping</p>