Building a K-nearest neighbors classifier

A K-nearest neighbors classifier is a classification model that uses the K-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. The value of K will depend on the problem at hand. Let's see how to build a classifier using this model.

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 ...