code: # Toolkit used for Classification
  
  # Importing Libraries
  from sklearn.linear_model import LogisticRegression
  from sklearn.svm import SVC
  from sklearn.ensemble import RandomForestClassifier
  
  # Logistic Regression Classification
  def logRegress(X_train, y_train, X_test, y_test):
      # Fitting Logistic Regression to the Training set
      classifier = LogisticRegression(random_state = 0)
      classifier.fit(X_train, y_train)
      
      # Predicting the Test set results
      y_pred = classifier.predict(X_test)
  
      # Counting accurate responses
      cm = numClose(y_test, y_pred)
  
      return [y_pred, cm]
  
  # SVM Classificaiton
  def svm(X_train, y_train, X_test, y_test):
      # Fitting SVM to the Training set
      classifier = SVC(kernel = 'linear', random_state = 0)
      classifier.fit(X_train, y_train)
      
      # Predicting the Test set results
      y_pred = classifier.predict(X_test)
      
      # Counting accurate responses
      cm = numClose(y_test, y_pred)
  
      return [y_pred, cm]
  
  # Random Forest Classification
  def rfor(X_train, y_train, X_test, y_test):
      # Fitting Random Forest Classification to the Training set
      classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)
      classifier.fit(X_train, y_train)
  
      # Predicting the Test set results
      y_pred = classifier.predict(X_test)
  
      # Counting accurate responses
      cm = numClose(y_test, y_pred)
      
      return [y_pred, cm]
  
  # Helper Method to Calculate num close
  def numClose(y_test, y_pred):
      result = 0;
      for pos in range(0, len(y_test)):
          if(y_test[pos] == y_pred[pos]):
             # or
             #y_test[pos] == y_pred[pos] + 1 or
             #y_test[pos] == y_pred[pos] -1):
              result += 1
      
      return result
apis: sklearn.ensemble.RandomForestClassifier sklearn.svm.SVC sklearn.linear_model.LogisticRegression