Extracting the nearest neighbors

Recommender systems employ the concept of nearest neighbors to find good recommendations. The name nearest neighbors refers to the process of finding the closest data points to the input point from the given dataset. This is frequently used to build classification systems that classify a data point based on the proximity of the input data point to various classes. Let's see how to find the nearest neighbors for a given data point.

First, create a new Python file and import the following packages:

import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import NearestNeighbors

Define sample 2D data points:

# Input data
X = np.array([[2.1, 1.3], [1.3, 3.2], [2.9, 2.5], [2.7, 5.4], [3.8, 0.9], 
        [7.3, 2.1], [4.2, 6.5], [3.8, 3.7], [2.5, 4.1], [3.4, 1.9],
        [5.7, 3.5], [6.1, 4.3], [5.1, 2.2], [6.2, 1.1]])

Define the number of nearest neighbors you want to extract:

# Number of...