A K-Nearest Neighbors classifier is a classification model that uses the nearest neighbors algorithm to classify a given data point. The algorithm finds the K closest data points in the training dataset to identify the category of the input data point. It will then assign a class to this data point based on a majority vote. From the list of those K data points, we look at the corresponding classes and pick the one with the highest number of votes. Let's see how to build a classifier using this model. The value of K depends on the problem at hand.

Create a new Python file and import the following packages:

import numpy as np 
import matplotlib.pyplot as plt 
import matplotlib.cm as cm 
from sklearn import neighbors, datasets 

Load the input data from data.txt. Each line contains comma-separated values and the data contains four classes:

# Load input data 
input_file = 'data.txt' 
data = np.loadtxt(input_file, delimiter=...