Solution :

K-Nearest Neighbor(KNN) Algorithm:

The K-Nearest Neighbors (KNN) algorithm is a supervised machine learning method employed to tackle classification and regression problems.

K-NN algorithm assumes the similarity between the new case/data and available cases and put the new case into the category that is most similar to the available categories.

K-NN algorithm stores all the available data and classifies a new data point based on the similarity. This means when new data appears then it can be easily classified into a well suite category by using K- NN algorithm.

K-NN algorithm can be used for Regression as well as for Classification but mostly it is used for the Classification problems.

In this program, we are going to use iris dataset.And this dataset Split into training(70%) and test set(30%).

The iris dataset conatins the following features

- sepal length (cm)
- sepal width (cm)
- petal length (cm)
- petal width (cm)

Library Installation:

To install required library files, Open Command Prompt or Terminal and execute the following commands

$ pip install scikit-learn

Source Code:

File Name: Knn.py

from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
import random

data_iris = load_iris()
label_target = data_iris.target_names
print()

print("Sample Data from Iris Dataset")
print("*"*30)
for i in range(10):
	rn = random.randint(0,120)
	print(data_iris.data[rn],"===>",label_target[data_iris.target[rn]])

X = data_iris.data
y = data_iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3,random_state=1)

print("The Training dataset length: ",len(X_train))
print("The Testing dataset length: ",len(X_test))
try:
	nn = int(input("Enter number of neighbors :"))
	knn = KNeighborsClassifier(nn)
	knn.fit(X_train, y_train)
	# to display the score
	print("The Score is :",knn.score(X_test, y_test))
	# To get test data from the user
	test_data = input("Enter Test Data :").split(",")
	for i in range(len(test_data)):
		test_data[i] = float(test_data[i])
	print()
	v = knn.predict([test_data])
	print("Predicted output is :",label_target[v])
except:
	print("Please supply valid input......")

Output:

Sample Run:
--------------
$ python3 Knn.py
Sample Data from Iris Dataset
******************************
[6.1 2.8 4.  1.3] ===> versicolor
[4.6 3.1 1.5 0.2] ===> setosa
[5.8 2.6 4.  1.2] ===> versicolor
[6.9 3.2 5.7 2.3] ===> virginica
[6.7 3.1 4.7 1.5] ===> versicolor
[4.7 3.2 1.3 0.2] ===> setosa
[5.  3.  1.6 0.2] ===> setosa
[6.4 2.9 4.3 1.3] ===> versicolor
[5.5 2.4 3.7 1. ] ===> versicolor
[5.8 2.6 4.  1.2] ===> versicolor
The Training dataset length:  105
The Testing dataset length:  45
Enter number of neighbors :9
The Score is : 0.9777777777777777
Enter Test Data :5.3,2.2,3.2,1.5

Predicted output is : ['versicolor']