Book Image

Artificial Intelligence with Python - Second Edition

By : Prateek Joshi
Book Image

Artificial Intelligence with Python - Second Edition

By: Prateek Joshi

Overview of this book

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
Table of Contents (26 chapters)
24
Other Books You May Enjoy
25
Index

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 ...