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)

Building an event predictor

Let's apply all of this knowledge from this chapter to a real-world problem. We will build an SVM to predict the number of people going in and out of a building. The dataset is available at https://archive.ics.uci.edu/ml/datasets/CalIt2+Building+People+Counts. We will use a slightly modified version of this dataset so that it's easier to analyze. The modified data is available in the building_event_binary.txt and the building_event_multiclass.txt files that are already provided to you. In this recipe, we will learn how to build an event predictor.

Getting ready

Let's understand the data format before we start building the model. Each line in building_event_binary.txt consists of...