Book Image

Practical Machine Learning Cookbook

By : Atul Tripathi
Book Image

Practical Machine Learning Cookbook

By: Atul Tripathi

Overview of this book

Machine learning has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations. The first half of the book provides recipes on fairly complex machine-learning systems, where you’ll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more. The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one.
Table of Contents (21 chapters)
Practical Machine Learning Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
14
Case Study - Forecast of Electricity Consumption

Introduction


Discriminant analysis is used to distinguish distinct sets of observations and allocate new observations to previously defined groups. For example, if a study was to be carried out in order to investigate the variables that discriminate between fruits eaten by (1) primates, (2) birds, or (3) squirrels, the researcher could collect data on numerous fruit characteristics of those species eaten by each of the animal groups. Most fruits will naturally fall into one of the three categories. Discriminant analysis could then be used to determine which variables are the best predictors of whether a fruit will be eaten by birds, primates, or squirrels. Discriminant analysis is commonly used in biological species classification, in medical classification of tumors, in facial recognition technologies, and in the credit card and insurance industries for determining risk. The main goals of discriminant analysis are discrimination and classification. The assumptions regarding discriminant...