The **k-nearest neighbors (KNN)** algorithm is a [supervised machine learning algorithm](https://www.educative.io/answers/supervised-learning-algorithms). 

KNN assumes that similar things exist in close proximity. In data science, it implies that similar data points are close to each other. KNN uses similarity to calculate the distance between points on a graph.

# How it works
- The algorithm calculates the distance of a new data point to all other training data points. The distance can be of any type, e.g., Euclidean, Manhattan, etc. 
- The algorithm then selects the k-nearest data points, where **k** can be any integer. It makes its selection based on the proximity to other data points regardless of what feature the numerical values represent. 
- Finally, it assigns the data point to the class where similar data points lie.

 

# Code
The `KNeighborsClassifier` function can be imported from the `sklearn` library. The function takes the value for `n_neighbors` as a parameter. This specifies the value for *k*. The below example demonstrates the algorithm on the *Iris* dataset.

from sklearn.neighbors import KNeighborsClassifier 
from sklearn.model_selection import train_test_split 
from sklearn.datasets import load_iris 
  
# Loading data 
irisData = load_iris() 
  
# Create feature and target arrays 
X = irisData.data 
y = irisData.target 
  
# Split into training and test set 
X_train, X_test, y_train, y_test = train_test_split( 
             X, y, test_size = 0.2, random_state=42) 
  
knn = KNeighborsClassifier(n_neighbors=7) # k = 7
  
knn.fit(X_train, y_train) 
  
# Calculate the accuracy of the model 
print("Accuracy:", knn.score(X_test, y_test)) 

Using the k-nearest neighbors algorithm in Python

The k-nearest neighbors (KNN) algorithm is a supervised machine learning algorithm. KNN assumes that similar things exist in close proximity. In data science, it implies that similar data points are close to each other. KNN uses similarity to calculate the distance between points on a graph. How it works  The algorithm calculates the distance of a new data point to all other training data points. The distance can be of any type, e.g., Euclidean, Manhattan, etc. The algorithm then selects the k-nearest data points, where k can be any integer. It makes its selection based on the proximity to other data points regardless of what feature the numerical values represent. Finally, it assigns the data point to the class where similar data points lie.  