K-nearest neighbors is a classification algorithm that is used to classify a given test data according to the surrounding data.

KNN works by calculating the distance of the test data with all the given data and selecting the first K data which are nearest to the test data. After that, the test data is classified according to the class that appears the most in the K-selected data.

For example:

Suppose there are 6 points on a 1D plane: [1, 2, 5, 8, 9, 11]. Let the points be classified with 0 and 1.

So, assigning random classes to points: [1:0, 2:0, 5:1, 8:0, 9:1, 11:0].

Let’s take the value of K as 3.

Now, let’s take the input data as 10. We will now apply the K-nearest neighbors algorithm to this input data. So, 3-nearest neighbors of 10 will be selected, which are [8:0, 9:1, 11:0]. Now, to assign a class to the input data, we will find which class occurs the maximum time among the K selected points. Since points 8 and 11 are of class 0, and point 9 is of class 1, input data will be assigned class 0 since it occurs the maximum time.

In this post, we will write the program for the K-nearest neighbors classifier. We will use python to write this program and we will not use any libraries.

### Input

We have the dataset given below. It consists of 2D points and a class to which they are classified.

data = [ [5,2,0], [2,4,0], [2,5,1], [4,6,1], [5,2,1], [1,5,1], [6,7,0], [4,2,0], [6,4,0], [9,2,0], [4,5,1], [1,6,1], [4,7,0], [3,6,0], [1,1,0], [8,4,1], [8,7,1], [7,2,1], [2,2,0], [2,1,0], [1,2,0], [1,4,1], [2,6,1], [7,7,0], [7,4,0], [3,4,1], [1,4,1] ] x = [i[0] for i in data] y = [i[1] for i in data] label = [i[2] for i in data] import matplotlib.pyplot as plt plt.scatter(x,y,c=label) plt.show()

### Distance function

Now, we will define a function to calculate the distance between two points.

import math def dist(testRow, trainRow): d = 0.0 for i in range(0,len(trainRow)-1): d += (testRow[i]-trainRow[i])**2 return math.sqrt(d)

### Inputting test data

Now we will input the test data from the user.

print("Enter the point to classify") test = [int(i) for i in input().split()] print("Enter the k") k = int(input())

Enter the point to classify 3 7 Enter the k 3

plt.scatter(x,y,c=label) plt.scatter(test[0],test[1],c='red') plt.show()

### Algorithm

Now, we will apply the K-nearest neighbor classification algorithm.

d = list() for row in data: temp = dist(test,row) d.append((temp,row)) d.sort(key = lambda x: x[0]) knn = list() print("K nearest neighbours") for i in range(k): print("point: ("+str(d[i][1][0])+", "+str(d[i][1][1])+") with distance: "+str(d[i][0])+" and class: "+str(d[i][1][-1])) knn.append(d[i][1])

K nearest neighbours point: (4, 7) with distance: 1.0 and class: 0 point: (3, 6) with distance: 1.0 and class: 0 point: (4, 6) with distance: 1.4142135623730951 and class: 1

### Predicting the class

After applying the algorithm, we can predict the class of the test data.

labels = [label[-1] for label in knn] pred = max(set(labels), key=labels.count) print('prediction: '+str(pred))

prediction: 0

That’s it. The classification of test data will be calculated according to the K-nearest neighbors.

### Complete code

data = [ [5,2,0], [2,4,0], [2,5,1], [4,6,1], [5,2,1], [1,5,1], [6,7,0], [4,2,0], [6,4,0], [9,2,0], [4,5,1], [1,6,1], [4,7,0], [3,6,0], [1,1,0], [8,4,1], [8,7,1], [7,2,1], [2,2,0], [2,1,0], [1,2,0], [1,4,1], [2,6,1], [7,7,0], [7,4,0], [3,4,1], [1,4,1] ] x = [i[0] for i in data] y = [i[1] for i in data] label = [i[2] for i in data] import matplotlib.pyplot as plt plt.scatter(x,y,c=label) plt.show() import math def dist(testRow, trainRow): d = 0.0 for i in range(0,len(trainRow)-1): d += (testRow[i]-trainRow[i])**2 return math.sqrt(d) print("Enter the point to classify") test = [int(i) for i in input().split()] print("Enter the k") k = int(input()) plt.scatter(x,y,c=label) plt.scatter(test[0],test[1],c='red') plt.show() d = list() for row in data: temp = dist(test,row) d.append((temp,row)) d.sort(key = lambda x: x[0]) knn = list() print("K nearest neighbours") for i in range(k): print("point: ("+str(d[i][1][0])+", "+str(d[i][1][1])+") with distance: "+str(d[i][0])+" and class: "+str(d[i][1][-1])) knn.append(d[i][1]) labels = [label[-1] for label in knn] pred = max(set(labels), key=labels.count) print('prediction: '+str(pred))

### Other Machine Learning algorithms:

- Naive Bayes Classification
- K Nearest Neighbors
- Linear Regression
- K Means Clustering
- Apriori Algorithm
- Principal Component Analysis (PCA)

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