# importing necessary libraries
from sklearn import datasets
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
  
# loading the iris dataset
iris = datasets.load_iris()
  
# X -> features, y -> label
X = iris.data
y = iris.target
  
# dividing X, y into train and test data
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0)
  
# training a DescisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
dtree_model = DecisionTreeClassifier(max_depth = 2).fit(X_train, y_train)
dtree_predictions = dtree_model.predict(X_test)
  
# creating a confusion matrix
cm = confusion_matrix(y_test, dtree_predictions)
print("DescisionTreeClassifier", cm)


# training a linear SVM classifier
from sklearn.svm import SVC
svm_model_linear = SVC(kernel = 'linear', C = 1).fit(X_train, y_train)
svm_predictions = svm_model_linear.predict(X_test)
  
# model accuracy for X_test  
accuracy = svm_model_linear.score(X_test, y_test)
  
# creating a confusion matrix
cm = confusion_matrix(y_test, svm_predictions)
print("linear SVM: ", cm)


# training a KNN classifier
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors = 7).fit(X_train, y_train)
  
# accuracy on X_test
accuracy = knn.score(X_test, y_test)
print(accuracy)
  
# creating a confusion matrix
knn_predictions = knn.predict(X_test) 
cm = confusion_matrix(y_test, knn_predictions)
print("KNN:\n", cm)


# training a Naive Bayes classifier
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB().fit(X_train, y_train)
gnb_predictions = gnb.predict(X_test)
  
# accuracy on X_test
accuracy = gnb.score(X_test, y_test)
print(accuracy)
  
# creating a confusion matrix
cm = confusion_matrix(y_test, gnb_predictions)
print("NB:\n", cm)