title: Support Vector Regression: TypeError: must be real number, not str
body: <p>I'm trying to use sklearn SVR for a small dataset. I'm getting error when I try to fit() data</p>
  
  <blockquote>
    <p>TypeError: must be real number, not str</p>
  </blockquote>
  
  <p>Here is my data and code:</p>
  
  <pre><code>     Revenue   Units      Rev_per_unit
  0    147754.0  8333629.0  17.73
  1    126146.0  7601824.0  16.59
  2    152385.0  8487163.0  17.95
  3    138703.0  8170619.0  16.98
  4    157860.0  8589258.0  18.38
  5    159981.0  8634245.0  18.53
  6    160006.0  9063836.0  17.65
  7    143556.0  9315878.0  15.41
  8    129380.0  9012887.0  14.35
  9    135771.0  9370077.0  14.49
  10   129593.0  9018405.0  14.37
  11   123941.0  9410973.0  13.17
  
  from sklearn.svm import SVR
  df = pd.read_csv('revenue.csv')
  X = df[['Revenue', 'Unit']]
  y = df['Rev_per_unit']
  X_train, X_test, y_train, y_test = train_test_split(X, y)
  svr_reg = SVR(gamma='scale', C=1.0, epsilon=0.2)
  svr_reg.fit(X_train, y_train)
  </code></pre>
  
  <p>I understand the error however when I use the same data for <code>LinearRegression()</code>, I do not get any error for the same X_train, y_train.</p>