K Nearest Neighbor Algorithm

Algorithm

• Step-1: Select the number K of the neighbors
• Step-2: Calculate the Euclidean distance of K number of neighbors
• Step-3: Take the K nearest neighbors as per the calculated Euclidean distance.
• Step-4: Among these k neighbors, count the number of the data points in each category.
• Step-5: Assign the new data points to that category for which the number of the neighbor is maximum.
• Step-6: Our model is ready.

Data Sample

X = [
    [167, 51],
    [182, 62],
    [176, 69],
    [173, 64],
    [172, 65],
    [174, 56],
    [169, 58],
    [173, 57],
    [170, 55]
]
y = ['Underweight', 'Normal', 'Normal', 'Normal', 'Normal', 'Underweight', 'Normal', 'Normal', 'Normal']
x = [[170, 57]]

Euclidean

$$d(x,y) = \sqrt{\sum{(x_i - y_i)^2}}$$

Step 1:

Select k = 5

Calculate distance d(X, x)

from sklearn.metrics.pairwise import euclidean_distances
euclidean_distances(X, x)

Build model

from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5)
classifier.fit(X, y)

Predict

x = [[170, 57]]
classifier.predict(x)

Predict Probability

classifier.predict_proba(x)

Accuracy score

classifier.score(X, y)

Data Sample