Book Image

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

By : Tarek Amr
Book Image

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

By: Tarek Amr

Overview of this book

Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits. The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You’ll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you’ll gain a thorough understanding of its theory and learn when to apply it. As you advance, you’ll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms. By the end of this machine learning book, you’ll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You’ll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production.
Table of Contents (18 chapters)
1
Section 1: Supervised Learning
8
Section 2: Advanced Supervised Learning
13
Section 3: Unsupervised Learning and More

Getting the click prediction dataset

Usually, a small percentage of people who see an advertisement click on it. In other words, the percentage of samples in a positive class in such an instance can be just 1% or even less. This makes it hard to predict the click-through rate (CTR) since the training data is highly imbalanced. In this section, we are going to use a highly imbalanced dataset from the Knowledge Discovery in Databases (KDD) Cup.

The KDD Cup is an annual competition organized by the ACM Special Interest Group on Knowledge Discovery and Data Mining. In 2012, they released a dataset for the advertisements shown alongside the search results in a search engine. The aim of the competitors was to predict whether a user will click on each ad or not. A modified version of the data has been published on the OpenML platform (https://www.openml.org/d/1220). The CTR in the modified dataset is 16.8%. This is our positive class. We can also call it the minority class since...