Book Image

Python Machine Learning Cookbook - Second Edition

By : Giuseppe Ciaburro, Prateek Joshi
Book Image

Python Machine Learning Cookbook - Second Edition

By: Giuseppe Ciaburro, Prateek Joshi

Overview of this book

This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning. By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples.
Table of Contents (18 chapters)

Estimating traffic

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

Getting ready

We will use the dataset available at https://archive.ics.uci.edu/ml/datasets/Dodgers+Loop+Sensor. This is a dataset that counts the number of cars passing by during baseball games at the 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...