Book Image

Python Machine Learning Cookbook

By : Prateek Joshi, Vahid Mirjalili
Book Image

Python Machine Learning Cookbook

By: Prateek Joshi, Vahid Mirjalili

Overview of this book

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Table of Contents (19 chapters)
Python Machine Learning Cookbook
About the Author
About the Reviewer

Estimating traffic

An interesting application of SVMs is to predict the traffic, based on related data. In the previous recipe, we used an SVM as a classifier. In this recipe, we will use it as a regressor to estimate the traffic.

Getting ready

We will use the dataset available at This is a dataset that counts the number of cars passing by during baseball games at Los Angeles Dodgers home stadium. We will use a slightly modified form of that dataset so that it's easier to analyze. You can use the traffic_data.txt file already provided to you. Each line in this file contains comma-separated strings formatted in the following manner:

  • Day

  • Time

  • The opponent team

  • Whether or not a baseball game is going on

  • The number of cars passing by

How to do it…

  1. Let's see how to build an SVM regressor. We will use that's already provided to you as a reference. Create a new Python file, and add the following lines:

    # SVM regressor to estimate traffic...