Create 9.py

Write a program to implement k-Nearest Neighbour algorithm to classify the iris data set. Print both correct and wrong predictions. Java/Python ML library classes can be used for this problem.

Author: fatalcoder524

9.py

@@ -0,0 +1,19 @@
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import classification_report, confusion_matrix
+from sklearn import datasets
+iris=datasets.load_iris()
+x = iris.data
+y = iris.target
+print ('sepal-length', 'sepal-width', 'petal-length', 'petal-width')
+print(x)
+print('class: 0-Iris-Setosa, 1- Iris-Versicolour, 2- Iris-Virginica')
+print(y)
+x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.3)
+classifier = KNeighborsClassifier(n_neighbors=5)
+classifier.fit(x_train, y_train)
+y_pred=classifier.predict(x_test)
+print('Confusion Matrix')
+print(confusion_matrix(y_test,y_pred))
+print('Accuracy Metrics')
+print(classification_report(y_test,y_pred))