text: import pandas as pd
  from sklearn.svm import SVC
  from sklearn import model_selection
  
  url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
  names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
  dataset = pd.read_csv(url, names=names)
  
  #Features and labels
  array = dataset.values
  #X are the features
  X = array[:,0:4]
  #Y are the labels for the features
  Y = array[:,4]
  
  #Spliting into train and test features and labels
  seed = 7
  test_size = 0.20
  features_train, features_test, labels_train, labels_test = model_selection.train_test_split(X, Y, test_size=test_size, random_state=seed)
  
  #Defining the classifier
  clf = SVC(kernel='linear')
  clf.fit(features_train, labels_train)
  accuacy = clf.score(features_test, labels_test)
  pred = clf.predict([[7.5,3.1,4.9,1.2]])
  print(accuacy)
  print(pred)
  