K Nearest Neighbors(KNN)

What is KNN?

KNN categorizes new values into the category that has a majority among the K nearest neighbors.

To

Steps of KNN

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• Step 1.
  Choose the number K of neighbors.
  Let’s assume that K is 5.
  

• Step 2.
  Take the K nearest neighbors of the new data point according to the Euclidean distance (most commonly used), Manhattan distance, or any other distance metric.
  

• Step 3.
  Among these K neighbors, count the number of data points in each category.
  In the example:
  
  • Category 1: 3 neighbors
    
  • Category 2: 2 neighbors
    
    
• Step 4.
  Assign the new data point to the category with the most neighbors.
  

Example

Code

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from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)


classifier = KNeighborsClassifier(n_neighbors=5, metric='minkowski', p=2) #metric : algorithm to determine the distance between two points.
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)

Result

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Implementation

• KNN