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)
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Estimating housing prices using a Support Vector Regressor

Let's see how to use the SVM concept to build a regressor to estimate housing prices. We will use the dataset available in sklearn where each datapoint is defined by 13 attributes.

Our goal is to estimate housing prices based on these attributes. Create a new Python file and import the following packages:

import numpy as np
from sklearn import datasets
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error, explained_variance_score
from sklearn.utils import shuffle

Load the housing dataset:

# Load housing data
data = datasets.load_boston()

Let's shuffle the data so that we don't bias our analysis:

# Shuffle the data
X, y = shuffle(,, random_state=7)

Split the dataset into training and testing in an 80/20 format:

# Split the data into training and testing datasets 
num_training = int(0.8 * len(X))