# Machine Learning – Tutorial 17

### Writing our own K Nearest Neighbours in Code

https://pythonprogramming.net/coding-k-nearest-neighbors-machine-learning-tutorial/

```# imports
import numpy as np
from math import sqrt
import matplotlib.pyplot as plt
import warnings
from collections import Counter

# To set charts to save as images we need to change the default behaviour
from matplotlib import style # inport style to change default behaviour of plot
style.use('ggplot') # use ggplot

dataset = {'k':[[1,2],[2,3],[3,1]], 'r':[[6,5],[7,7],[8,6]]} # defines as a dictionary 2 classes (k&amp;r) with 3 features (lists of lists)
new_features = [5,7]

## Expanded one line for loop
#for i in dataset:
#    for ii in dataset[i]:
#        plt.scatter(ii,ii,s=100, color=i)

# define function
def K_nearest_neighbours(data, predict, k=3):
if len(data) >= k:
warnings.warn('K is set to value less than total voting groups!')
distances = []
for group in data:
for features in data[group]:
euclidean_distance = np.linalg.norm(np.array(features)-np.array(predict))
distances.append([euclidean_distance, group])

votes = [i for i in sorted(distances) [:k]]