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


Hierarchical clustering: One of the most important methods in unsupervised learning is Hierarchical clustering. In Hierarchical clustering for a given set of data points, the output is produced in the form of a binary tree (dendrogram). In the binary tree, the leaves represent the data points while internal nodes represent nested clusters of various sizes. Each object is assigned a separate cluster. Evaluation of all the clusters takes place based on a pairwise distance matrix. The distance matrix will be constructed using distance values. The pair of clusters with the shortest distance must be considered. The identified pair should then be removed from the matrix and merged together. The merged clusters' distance must be evaluated with the other clusters and the distance matrix should be updated. The process is to be repeated until the distance matrix is reduced to a single element.

An ordering of the objects is produced by hierarchical clustering. This helps with informative...