title: ValueError: X.shape[1] = 2 should be equal to 13, the number of features at training time
body: <p>I am trying to predict lung cancer data by using SVM document classifier using scikit-learn and I am using the following code but getting some error. I have used <code>matplotlib.pyplot as plt</code> for data plot but getting error. </p>
  
  <p>Here I am using lung cancer data risk factor wise. </p>
  
  <p>Input File</p>
  
  <pre><code>GENDER  AGE SMOKING YELLOW_FINGERS  ANXIETY PEER_PRESSURE   CHRONIC DISEASE FATIGUE     ALLERGY     WHEEZING    ALCOHOL CONSUMING   COUGHING    SHORTNESS OF BREATH SWALLOWING DIFFICULTY   CHEST PAIN  LUNG_CANCER
  F   59  0   0   0   1   0   1   0   1   0   1   1   0   1   0
  F   63  0   1   0   0   0   0   0   1   0   1   1   0   0   0
  F   75  0   1   0   0   1   1   1   1   0   1   1   0   0   1
  M   69  0   1   1   0   0   1   0   1   1   1   1   1   1   1
  M   74  1   0   0   0   1   1   1   0   0   0   1   1   1   1
  M   63  1   1   1   0   0   0   0   0   1   0   0   1   1   0
  </code></pre>
  
  <p><strong>Script SVM</strong></p>
  
  <pre><code># Support Vector Machine (SVM)
  
  # Importing the libraries
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  
  # Importing the dataset
  dataset = pd.read_csv('C:/Users/Vishnu/Desktop/Lung Cancer/lung_cancer.csv')
  X = dataset.iloc[:, [2,3,4,5,6,7,8,9,10,11,12,13,14]].values
  y = dataset.iloc[:, 15].values
  
  # Splitting the dataset into the Training set and Test set
  from sklearn.cross_validation import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
  
  # Feature Scaling
  from sklearn.preprocessing import StandardScaler
  sc = StandardScaler()
  X_train = sc.fit_transform(X_train)
  X_test = sc.transform(X_test)
  
  # Fitting SVM to the Training set
  from sklearn.svm import SVC
  classifier = SVC(kernel = 'linear', random_state = 0)
  classifier.fit(X_train, y_train)
  
  # Predicting the Test set results
  y_pred = classifier.predict(X_test)
  
  # Making the Confusion Matrix
  from sklearn.metrics import confusion_matrix
  cm = confusion_matrix(y_test, y_pred)
  
  # Visualising the Training set results
  from matplotlib.colors import ListedColormap
  X_set, y_set = X_train, y_train
  X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                       np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
  plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
               alpha = 0.75, cmap = ListedColormap(('red', 'green')))
  plt.xlim(X1.min(), X1.max())
  plt.ylim(X2.min(), X2.max())
  for i, j in enumerate(np.unique(y_set)):
      plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                  c = ListedColormap(('red', 'green'))(i), label = j)
  plt.title('SVM (Training set)')
  plt.xlabel('Age')
  plt.ylabel('Lung Cancer Risk Factor')
  plt.legend()
  plt.show()
  
  # Visualising the Test set results
  from matplotlib.colors import ListedColormap
  X_set, y_set = X_test, y_test
  X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                       np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
  plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
               alpha = 0.75, cmap = ListedColormap(('red', 'green')))
  plt.xlim(X1.min(), X1.max())
  plt.ylim(X2.min(), X2.max())
  for i, j in enumerate(np.unique(y_set)):
      plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                  c = ListedColormap(('red', 'green'))(i), label = j)
  plt.title('SVM (Test set)')
  plt.xlabel('Age')
  plt.ylabel('Lung Cancer Risk Factor')
  plt.legend()
  plt.show()
  </code></pre>
  
  <p><strong>Error</strong></p>
  
  <pre><code>ValueError: X.shape[1] = 2 should be equal to 13, the number of features at training time
  </code></pre>
  
  <p>In this like I getting error</p>
  
  <pre><code>plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
           alpha = 0.75, cmap = ListedColormap(('red', 'green')))
  </code></pre>
  
  <p>Why I am getting error, Please suggest me something. Thank you in adavance.</p>
  
  <p><strong>Edit_1</strong></p>
  
  <p>SVM Test set output graph</p>
  
  <p><img src="https://i.stack.imgur.com/cdjTn.png" alt="SVM Test set output graph"></p>
  
  <p>SVM Training Set output graph</p>
  
  <p><img src="https://i.stack.imgur.com/bD5EB.png" alt="SVM Training Set output graph"></p>
  
  <p>Can anyone please let me know. Is this a correct output or not?</p>
  
  <p>Thanks in advance </p>