title: Multivariable/Multiple Linear Regression in Scikit Learn?
body: <p>I have a dataset (dataTrain.csv &amp; dataTest.csv) in .csv file with this format:</p>
  <pre><code>Temperature(K),Pressure(ATM),CompressibilityFactor(Z)
  273.1,24.675,0.806677258
  313.1,24.675,0.888394713
  ...,...,...
  </code></pre>
  <p>And able to build a regression model and prediction with this code:</p>
  <pre><code>import pandas as pd
  from sklearn import linear_model
  
  dataTrain = pd.read_csv(&quot;dataTrain.csv&quot;)
  dataTest = pd.read_csv(&quot;dataTest.csv&quot;)
  # print df.head()
  
  x_train = dataTrain['Temperature(K)'].reshape(-1,1)
  y_train = dataTrain['CompressibilityFactor(Z)']
  
  x_test = dataTest['Temperature(K)'].reshape(-1,1)
  y_test = dataTest['CompressibilityFactor(Z)']
  
  ols = linear_model.LinearRegression()
  model = ols.fit(x_train, y_train)
  
  print model.predict(x_test)[0:5]
  </code></pre>
  <p>However, what I want to do is multivariable regression. So, the model will be <code>CompressibilityFactor(Z) = intercept + coef*Temperature(K) + coef*Pressure(ATM)</code></p>
  <p>How to do that in scikit-learn?</p>