lab9.py

from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn import datasets        # Load dataset
iris=datasets.load_iris()
print("Iris Data set loaded...")        # Split the data into train and test samples
x_train, x_test, y_train, y_test = train_test_split(iris.data,iris.target,test_size=0.1)
print("Dataset is split into training and testing...")
print("Size of trainng data and its label",x_train.shape,y_train.shape)

print("Size of training data and its label",x_test.shape, y_test.shape)
 # Prints Label no. and their names
for i in range(len(iris.target_names)):
      print("Label", i , "-",str(iris.target_names[i]))
      # Create object of KNN classifier
      classifier = KNeighborsClassifier(n_neighbors=1)        # Perform Training
      classifier.fit(x_train, y_train)    # Perform testing
      y_pred=classifier.predict(x_test)        # Display the results
      print("Results of Classification using K-nn with K=1 ")
      for r in range(0,len(x_test)):
          print(" Sample:", str(x_test[r]), end='\t')
          print(" Actual-label:", str(y_test[r]), end='\t')
          print(" Predicted label:", str(y_pred[r]))
      print("Classification Accuracy :" , classifier.score(x_test,y_test));