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- Herausgeber: Packt Publishing
- Kategorie: Lebensstil
- Sprache: Englisch
Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well.
Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis.
By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem
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Seitenzahl: 213
Veröffentlichungsjahr: 2018
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Data Science Algorithms in a Week Second Edition
Copyright © 2018 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
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Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Commissioning Editor:Amey VarangaonkarAcquisition Editor: Joshua NadarContent Development Editor: Ronnel MathewTechnical Editor: Sneha HanchateCopy Editor: Safis EditingProject Coordinator: Nidhi JoshiProofreader: Safis EditingIndexer: Rekha NairGraphics: Tom ScariaProduction Coordinator: Aparna Bhagat
First published: August 2017 Second edition: October 2018
Production reference: 1311018
Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK.
ISBN 978-1-78980-607-6
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Contributors
About the author
Dávid Natingga graduated with a master's in engineering in 2014 from Imperial College London, specializing in artificial intelligence. In 2011, he worked at Infosys Labs in Bangalore, India, undertaking research into the optimization of machine learning algorithms. In 2012 and 2013, while at Palantir Technologies in USA, he developed algorithms for big data. In 2014, while working as a data scientist at Pact Coffee, London, he created an algorithm suggesting products based on the taste preferences of customers and the structures of the coffees. In order to use pure mathematics to advance the field of AI, he is a PhD candidate in Computability Theory at the University of Leeds, UK. In 2016, he spent 8 months at Japan's Advanced Institute of Science and Technology as a research visitor.
About the reviewers
Surendra Pepakayala is a hands-on, seasoned technology professional with over 20 years of experience in the US and India. He has built enterprise software products at startups and multinational companies and has built and sold a technology business after five years in operation. He currently consults for small to medium businesses, helping them leverage cloud, data science, machine learning, AI, and cutting-edge technologies to gain an advantage over their competition. In addition to being an advisory board member for a couple of startups in the technology space, he holds numerous coveted certifications such as TOGAF, CRISC, and CGEIT.Jen Stirrup is a data strategist and technologist, Microsoft Most Valuable Professional (MVP) and Microsoft Regional Director, tech community advocate, public speaker, blogger, published author, and keynote speaker. She is the founder of a boutique consultancy based in the UK, Data Relish, which focuses on delivering successful business intelligence and artificial intelligence solutions that add real value to customers worldwide. She has featured on the BBC as a guest expert on topics related to data.
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Table of Contents
Title Page
Copyright and Credits
Data Science Algorithms in a Week Second Edition
Packt Upsell
Why subscribe?
Packt.com
Contributors
About the author
About the reviewers
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Classification Using K-Nearest Neighbors
Mary and her temperature preferences
Implementation of the k-nearest neighbors algorithm
Map of Italy example – choosing the value of k
Analysis
House ownership – data rescaling
Analysis
Text classification – using non-Euclidean distances
Analysis
Text classification – k-NN in higher dimensions
Analysis
Summary
Problems
Mary and her temperature preference problems
Map of Italy – choosing the value of k
House ownership
Analysis
Naive Bayes
Medical tests – basic application of Bayes' theorem
Analysis
Bayes' theorem and its extension
Bayes' theorem
Proof
Extended Bayes' theorem
Proof
Playing chess – independent events
Analysis
Implementation of a Naive Bayes classifier
Playing chess – dependent events
Analysis
Gender classification – Bayes for continuous random variables
Analysis
Summary
Problems
Analysis
Decision Trees
Swim preference – representing data using a decision tree
Information theory
Information entropy
Coin flipping
Definition of information entropy
Information gain
Swim preference – information gain calculation
ID3 algorithm – decision tree construction
Swim preference – decision tree construction by the ID3 algorithm
Implementation
Classifying with a decision tree
Classifying a data sample with the swimming preference decision tree
Playing chess – analysis with a decision tree
Analysis
Classification
Going shopping – dealing with data inconsistencies
Analysis
Summary
Problems
Analysis
Random Forests
Introduction to the random forest algorithm
Overview of random forest construction
Swim preference – analysis involving a random forest
Analysis
Random forest construction
Construction of random decision tree number 0
Construction of random decision tree number 1
Constructed random forest
Classification using random forest
Implementation of the random forest algorithm
Playing chess example
Analysis
Random forest construction
Classification
Going shopping – overcoming data inconsistencies with randomness and measuring the level of confidence
Analysis
Summary
Problems
Analysis
Clustering into K Clusters
Household incomes – clustering into k clusters
K-means clustering algorithm
Picking the initial k-centroids
Computing a centroid of a given cluster
Using the k-means clustering algorithm on the household income example
Gender classification – clustering to classify
Analysis
Implementation of the k-means clustering algorithm
Input data from gender classification
Program output for gender classification data
House ownership – choosing the number of clusters
Analysis
Document clustering – understanding the number of k clusters in a semantic context
Analysis
Summary
Problems
Analysis
Regression
Fahrenheit and Celsius conversion – linear regression on perfect data
Analysis from first principles
Least squares method for linear regression
Analysis using the least squares method in Python
Visualization
Weight prediction from height – linear regression on real-world data
Analysis
Gradient descent algorithm and its implementation
Gradient descent algorithm
Implementation
Visualization – comparison of the least squares method and the gradient descent algorithm
Flight time duration prediction based on distance
Analysis
Ballistic flight analysis – non-linear model
Analysis
Analysis by using the least squares method in Python
Summary
Problems
Analysis
Time Series Analysis
Business profits – analyzing trends
Analysis
Analyzing trends using the least squares method in Python
Visualization
Conclusion
Electronics shop's sales – analyzing seasonality
Analysis
Analyzing trends using the least squares method in Python
Visualization
Analyzing seasonality
Conclusion
Summary
Problems
Analysis
Python Reference
Introduction
Python Hello World example
Comments
Data types
int
float
String
Tuple
List
Set
Dictionary
Flow control
Conditionals
For loop
For loop on range
For loop on list
Break and continue
Functions
Input and output
Program arguments
Reading and writing a file
Statistics
Basic concepts
Bayesian inference
Distributions
Normal distribution
Cross-validation
K-fold cross-validation
A/B testing
Glossary of Algorithms and Methods in Data Science
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Leave a review - let other readers know what you think
Preface
Data science is a discipline at the intersection of machine learning, statistics, and data mining with the objective of gaining new knowledge from existing data by means of algorithmic and statistical analysis. In this book, you will learn the seven most important ways in data science of analyzing the data. Each chapter first explains its algorithm or analysis as a simple concept, supported by a trivial example. Further examples and exercises are used to build and expand your knowledge of a particular type of analysis.
Who this book is for
This book is for aspiring data science professionals who are familiar with Python and have a background of sorts in statistics. Developers who are currently implementing one or two data science algorithms and who now want to learn more to expand their skillset will find this book quite useful.
What this book covers
Chapter 1, Classification Using K-Nearest Neighbors, classifies a data item based on the most similar k items.
Chapter 2, Naive Bayes, delves into Bayes' Theorem with a view to computing the probability a data item belonging to a certain class.
Chapter 3, Decision Trees, organizes your decision criteria into the branches of a tree, and uses a decision tree to classify a data item into one of the classes at the leaf node.
Chapter 4, Random Forests, classifies a data item with an ensemble of decision trees to improve the accuracy of the algorithm by reducing the negative impact of the bias.
Chapter 5, Clustering into K Clusters, divides your data into k clusters to discover the patterns and similarities between the data items and goes into how to exploit these patterns to classify new data.
Chapter 6, Regression, models phenomena in your data by using a function that can predict the values of the unknown data in a simple way.
Chapter 7, Time-Series Analysis, unveils the trends and repeating patterns in time-dependent data to predict the future of the stock market, Bitcoin prices, and other time events.
Appendix A, Python Reference, is a reference of the basic Python language constructs, commands, and functions used throughout the book.
Appendix B, Statistics, provides a summary of the statistical methods and tools that are useful to a data scientist.
Appendix C, Glossary of Algorithms and Methods in Data Science, provides a glossary of some of the most important and powerful algorithms and methods from the fields of data science and machine learning.
To get the most out of this book
To get the most out of this book, you require, first and foremost, an active attitude to think of the problems—a lot of new content is presented in the exercises at the end of the chapter in the section entitled Problems. You also then need to be able to run Python programs on the operating system of your choice. The author ran the programs on the Linux operating system using the command line
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Classification Using K-Nearest Neighbors
A nearest neighbor algorithm classifies a data instance based on its neighbors. The class of a data instance determined by the k-nearest neighbors algorithm is the class with the highest representation among the k-closest neighbors.
In this chapter, we will cover the following topics:
How to implement the basics of the
k-
NN algorithm using the example of Mary and her temperature preferences
How to
choose a correct
k
value so that the algorithm can perform correctly and with the highest degree of accuracy using the example of a map of Italy
How to rescale
values and prepare them for the k-NN algorithm using the example of house preferences
How
to choose a good metric to measure distances between data points
How to eliminate irrelevant dimensions in higher-dimensional space to ensure that the algorithm performs accurately using the text classification example
Mary and her temperature preferences
As an example, if we know that our friend, Mary, feels cold when it is 10°C, but warm when it is 25°C, then in a room where it is 22°C, the nearest neighbor algorithm would guess that our friend would feel warm, because 22 is closer to 25 than to 10.
Suppose that we would like to know when Mary feels warm and when she feels cold, as in the previous example, but in addition, wind speed data is also available when Mary is asked whether she feels warm or cold:
Temperature in °C
Wind speed in km/h
Mary's perception
10
0
Cold
25
0
Warm
15
5
Cold
20
3
Warm
18
7
Cold
20
10
Cold
22
5
Warm
24
6
Warm
We could represent the data in a graph, as follows:
Now, suppose we would like to find out how Mary feels when the temperature is 16°C with a wind speed of 3 km/h by using the 1-NN algorithm:
For simplicity, we will use a Manhattan metric to measure the distance between the neighbors on the grid. The Manhattan distance dMan of the neighbor N1=(x1,y1) from the neighbor N2=(x2,y2) is defined as dMan=|x1-x2|+|y1-y2|.
Let's label the grid with distances around the neighbors to see which neighbor with a known class is closest to the point we would like to classify:
We can see that the closest neighbor with a known class is the one with a temperature of 15°C (blue) and a wind speed of 5 km/h. Its distance from the point in question is three units. Its class is blue (cold). The closest red (warm) neighbour is at a distance of four units from the point in question. Since we are using the 1-nearest neighbor algorithm, we just look at the closest neighbor and, therefore, the class of the point in question should be blue (cold).
By applying this procedure to every data point, we can complete the graph, as follows:
Note that, sometimes, a data point might be the same distance away from two known classes: for example, 20°C and 6 km/h. In such situations, we could prefer one class over the other, or ignore these boundary cases. The actual result depends on the specific implementation of an algorithm.
