# Implementation Of KNN (From Scratch in PYTHON)

# Implementation Of KNN (From Scratch in PYTHON)

Implementation Of KNN (From Scratch in PYTHON) |

KNN classifier is one of the simplest but strong supervised machine learning algorithm. KNN can be used for both classification and regression problems. There are some libraries in python to implement KNN, which allows a programmer to make KNN model easily without using deep ideas of mathematics. But if we try to implement KNN from scratch it becomes a bit tricky.

**Algorithm for K-NN:**

Now we are all ready to dive into the code. let’s implement KNN from Scratch (Using pandas and Numpy only). We are going to classify the iris data into its different species by observing different 4 features: sepal length, sepal width, petal length, petal width. We have all together 150 observations(tuples) and we will make KNN classifying model on the basis of these observations.

Link to download iris dataset- iris.csv

```
import pandas as pd
import numpy as np
import operator
# loading data file into the program. give the location of your csv file
dataset = pd.read_csv("E:/input/iris.csv")
print(dataset.head()) # prints first five tuples of your data.
# making function for calculating euclidean distance
def E_Distance(x1, x2, length):
distance = 0
for x in range(length):
distance += np.square(x1[x] - x2[x])
return np.sqrt(distance)
# making function for defining K-NN model
def knn(trainingSet, testInstance, k):
distances = {}
length = testInstance.shape[1]
for x in range(len(trainingSet)):
dist = E_Distance(testInstance, trainingSet.iloc[x], length)
distances[x] = dist[0]
sortdist = sorted(distances.items(), key=operator.itemgetter(1))
neighbors = []
for x in range(k):
neighbors.append(sortdist[x][0])
Count = {} # to get most frequent class of rows
for x in range(len(neighbors)):
response = trainingSet.iloc[neighbors[x]][-1]
if response in Count:
Count[response] += 1
else:
Count[response] = 1
sortcount = sorted(Count.items(), key=operator.itemgetter(1), reverse=True)
return (sortcount[0][0], neighbors)
# making test data set
testSet = [[6.8, 3.4, 4.8, 2.4]]
test = pd.DataFrame(testSet)
# assigning different values to k
k = 1
k1 = 3
k2 = 11
# supplying test data to the model
result, neigh = knn(dataset, test, k)
result1, neigh1 = knn(dataset, test, k1)
result2, neigh2 = knn(dataset, test, k2)
# printing output prediction
print(result)
print(neigh)
print(result1)
print(neigh1)
print(result2)
print(neigh2)
```

The Output of above program is:

sepal.length sepal.width petal.length petal.width variety

0 5.1 3.5 1.4 0.2 Setosa

1 4.9 3.0 1.4 0.2 Setosa

2 4.7 3.2 1.3 0.2 Setosa

3 4.6 3.1 1.5 0.2 Setosa

4 5.0 3.6 1.4 0.2 Setosa

4

4

4

Virginica

[141]

Virginica

[141, 145, 110]

Virginica

[141, 145, 110, 115, 139, 147, 77, 148, 140, 112, 144]