In this Machine Learning from Scratch Tutorial, we are going to implement the K Nearest Neighbors (KNN) algorithm, using only built-in Python modules and numpy. We will also learn about the concept and the math behind this popular ML algorithm.

All algorithms from this course can be found on GitHub together with example tests.

## Implementation

```
import numpy as np
from collections import Counter
def euclidean_distance(x1, x2):
return np.sqrt(np.sum((x1 - x2)**2))
class KNN:
def __init__(self, k=3):
self.k = k
def fit(self, X, y):
self.X_train = X
self.y_train = y
def predict(self, X):
y_pred = [self._predict(x) for x in X]
return np.array(y_pred)
def _predict(self, x):
# Compute distances between x and all examples in the training set
distances = [euclidean_distance(x, x_train) for x_train in self.X_train]
# Sort by distance and return indices of the first k neighbors
k_idx = np.argsort(distances)[:self.k]
# Extract the labels of the k nearest neighbor training samples
k_neighbor_labels = [self.y_train[i] for i in k_idx]
# return the most common class label
most_common = Counter(k_neighbor_labels).most_common(1)
return most_common[0][0]
```