Book Image

Artificial Intelligence with Python

Book Image

Artificial Intelligence with Python

Overview of this book

Artificial Intelligence is becoming increasingly relevant in the modern world. By harnessing the power of algorithms, you can create apps which intelligently interact with the world around you, building intelligent recommender systems, automatic speech recognition systems and more. Starting with AI basics you'll move on to learn how to develop building blocks using data mining techniques. Discover how to make informed decisions about which algorithms to use, and how to apply them to real-world scenarios. This practical book covers a range of topics including predictive analytics and deep learning.
Table of Contents (23 chapters)
Artificial Intelligence with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Estimating housing prices using a Support Vector Regressor


Let's see how to use the SVM concept to build a regressor to estimate the housing prices. We will use the dataset available in sklearn where each data point is define, by 13 attributes. Our goal is to estimate the 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(data.data, data.target, 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)) 
X_train, y_train = X[:num_training...