KNN

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.

#import the required packages
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 trainng 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]), " Actual-label:", str(y_test[r]), " Predicted-label:", str(y_pred[r]))
print("Classification Accuracy :" , classifier.score(x_test,y_test)); 

 Ouput->

 
Iris Data set loaded...
Dataset is split into training and testing...
Size of trainng data and its label (135, 4) (135,)
Size of trainng data and its label (15, 4) (15,)
Label 0 - setosa
Label 1 - versicolor
Label 2 - virginica
Results of Classification using K-nn with K=1 
 Sample: [6.6 3.  4.4 1.4]  Actual-label: 1  Predicted-label: 1
 Sample: [6.9 3.1 4.9 1.5]  Actual-label: 1  Predicted-label: 1
 Sample: [5.5 2.6 4.4 1.2]  Actual-label: 1  Predicted-label: 1
 Sample: [5.1 3.8 1.6 0.2]  Actual-label: 0  Predicted-label: 0
 Sample: [6.1 2.8 4.  1.3]  Actual-label: 1  Predicted-label: 1
 Sample: [4.9 3.1 1.5 0.1]  Actual-label: 0  Predicted-label: 0
 Sample: [4.8 3.  1.4 0.1]  Actual-label: 0  Predicted-label: 0
 Sample: [5.4 3.  4.5 1.5]  Actual-label: 1  Predicted-label: 1
 Sample: [5.5 2.5 4.  1.3]  Actual-label: 1  Predicted-label: 1
 Sample: [6.  2.7 5.1 1.6]  Actual-label: 1  Predicted-label: 2
 Sample: [4.6 3.2 1.4 0.2]  Actual-label: 0  Predicted-label: 0
 Sample: [6.3 3.3 6.  2.5]  Actual-label: 2  Predicted-label: 2
 Sample: [6.  2.2 4.  1. ]  Actual-label: 1  Predicted-label: 1
 Sample: [5.4 3.9 1.7 0.4]  Actual-label: 0  Predicted-label: 0
 Sample: [5.2 3.5 1.5 0.2]  Actual-label: 0  Predicted-label: 0
Classification Accuracy : 0.9333333333333333

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