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    Data Science Algorithms in a Week
    Data Science Algorithms in a Week
    Data Science Algorithms in a Week
    Ebook361 pages2 hours

    Data Science Algorithms in a Week

    By David Natingga

    ()

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    About this ebook

    Build strong foundation of machine learning algorithms In 7 days.

    About This Book
    • Get to know seven algorithms for your data science needs in this concise, insightful guide
    • Ensure you're confident in the basics by learning when and where to use various data science algorithms
    • Learn to use machine learning algorithms in a period of just 7 days
    Who This Book Is For

    This book is for aspiring data science professionals who are familiar with Python and have a statistics background. It is ideal for developers who are currently implementing one or two data science algorithms and want to learn more to expand their skill set.

    What You Will Learn
    • Find out how to classify using Naive Bayes, Decision Trees, and Random Forest to achieve accuracy to solve complex problems
    • Identify a data science problem correctly and devise an appropriate prediction solution using Regression and Time-series
    • See how to cluster data using the k-Means algorithm
    • Get to know how to implement the algorithms efficiently in the Python and R languages
    In Detail

    Machine learning applications are highly automated and self-modifying, and they continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly. Data science helps you gain new knowledge from existing data through algorithmic and statistical analysis.

    This book will address the problems related to accurate and efficient data classification and prediction. Over the course of 7 days, you will be introduced to seven algorithms, along with exercises that will help you learn different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. You will then find out how to predict data based on the existing trends in your datasets.

    This book covers algorithms such as: k-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k-Means, Regression, and Time-series. On completion of the book, you will understand which machine learning algorithm to pick for clustering, classification, or regression and which is best suited for your problem.

    Style and approach

    Machine learning applications are highly automated and self-modifying which continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly.

    LanguageEnglish
    PublisherPackt Publishing
    Release dateAug 16, 2017
    ISBN9781787282742
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      Book preview

      Data Science Algorithms in a Week - David Natingga

      Data Science Algorithms in a Week

      Data Science Algorithms in a Week

      Data analysis, machine learning, and more

      Dávid Natingga

      BIRMINGHAM - MUMBAI

      Data Science Algorithms in a Week

      Copyright © 2017 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.

      Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

      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.

      First published: August 2017

      Production reference: 1080817

      Published by Packt Publishing Ltd.

      Livery Place

      35 Livery Street

      Birmingham

      B3 2PB, UK.

      ISBN 978-1-78728-458-6

      www.packtpub.com

      Credits

      About the Author

      Dávid Natingga graduated in 2014 from Imperial College London in MEng Computing with a specialization in Artificial Intelligence. In 2011, he worked at Infosys Labs in Bangalore, India, researching the optimization of machine learning algorithms. In 2012 and 2013, at Palantir Technologies in Palo Alto, USA, he developed algorithms for big data. In 2014, as a data scientist at Pact Coffee, London, UK, he created an algorithm suggesting products based on the taste preferences of customers and the structure of coffees. In 2017, he work at TomTom in Amsterdam, Netherlands, processing map data for navigation platforms.

      As a part of his journey to use pure mathematics to advance the field of AI, he is a PhD candidate in Computability Theory at, University of Leeds, UK. In 2016, he spent 8 months at Japan, Advanced Institute of Science and Technology, Japan, as a research visitor.

      Dávid Natingga married his wife Rheslyn and their first child will soon behold the outer world.

      I would like to thank Packt Publishing for providing me with this opportunity to share my knowledge and experience in data science through this book. My gratitude belongs to my wife Rheslyn who has been patient, loving, and supportive through out the whole process of writing this book.

      About the Reviewer

      Surendra Pepakayala is a seasoned technology professional and entrepreneur with over 19 years of experience in the US and India. He has broad experience in building enterprise/web software products as a developer, architect, software engineering manager, and product manager at both start-ups and multinational companies in India and the US. He is a hands-on technologist/hacker with deep interest and expertise in Enterprise/Web Applications Development, Cloud Computing, Big Data, Data Science, Deep Learning, and Artificial Intelligence.

      A technologist turned entrepreneur, after 11 years in corporate US, Surendra has founded an enterprise BI / DSS product for school districts in the US. He subsequently sold the company and started a Cloud Computing, Big Data, and Data Science consulting practice to help start-ups and IT organizations streamline their development efforts and reduce time to market of their products/solutions. Also, Surendra takes pride in using his considerable IT experience for reviving / turning-around distressed products / projects.

      He serves as an advisor to eTeki, an on-demand interviewing platform, where he leads the effort to recruit and retain world-class IT professionals into eTeki’s interviewer panel. He has reviewed drafts, recommended changes and formulated questions for various IT certifications such as CGEIT, CRISC, MSP, and TOGAF. His current focus is on applying Deep Learning to various stages of the recruiting process to help HR (staffing and corporate recruiters) find the best talent and reduce friction involved in the hiring process.

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      Table of Contents

      Preface

      What this book covers

      What you need for this book

      Who this book is for

      Conventions

      Reader feedback

      Customer support

      Downloading the example code

      Downloading the color images of this book

      Errata

      Piracy

      Questions

      Classification Using K Nearest Neighbors

      Mary and her temperature preferences

      Implementation of k-nearest neighbors algorithm

      Map of Italy example - choosing the value of k

      House ownership - data rescaling

      Text classification - using non-Euclidean distances

      Text classification - k-NN in higher-dimensions

      Summary

      Problems

      Naive Bayes

      Medical test - basic application of Bayes' theorem

      Proof of Bayes' theorem and its extension

      Extended Bayes' theorem

      Playing chess - independent events

      Implementation of naive Bayes classifier

      Playing chess - dependent events

      Gender classification - Bayes for continuous random variables

      Summary

      Problems

      Decision Trees

      Swim preference - representing data with 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 ID3 algorithm

      Implementation

      Classifying with a decision tree

      Classifying a data sample with the swimming preference decision tree

      Playing chess - analysis with decision tree

      Going shopping - dealing with data inconsistency

      Summary

      Problems

      Random Forest

      Overview of random forest algorithm

      Overview of random forest construction

      Swim preference - analysis with random forest

      Random forest construction

      Construction of random decision tree number 0

      Construction of random decision tree number 1

      Classification with random forest

      Implementation of random forest algorithm

      Playing chess example

      Random forest construction

      Construction of a random decision tree number 0:

      Construction of a random decision tree number 1, 2, 3

      Going shopping - overcoming data inconsistency with randomness and measuring the level of confidence

      Summary

      Problems

      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

      k-means clustering algorithm on household income example

      Gender classification - clustering to classify

      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

      Document clustering – understanding the number of clusters k in a semantic context

      Summary

      Problems

      Regression

      Fahrenheit and Celsius conversion - linear regression on perfect data

      Weight prediction from height - linear regression on real-world data

      Gradient descent algorithm and its implementation

      Gradient descent algorithm

      Visualization - comparison of models by R and gradient descent algorithm

      Flight time duration prediction from distance

      Ballistic flight analysis – non-linear model

      Summary

      Problems

      Time Series Analysis

      Business profit - analysis of the trend

      Electronics shop's sales - analysis of seasonality

      Analyzing trends using R

      Analyzing seasonality

      Conclusion

      Summary

      Problems

      Statistics

      Basic concepts

      Bayesian Inference

      Distributions

      Normal distribution

      Cross-validation

      K-fold cross-validation

      A/B Testing

      R Reference

      Introduction

      R Hello World example

      Comments

      Data types

      Integer

      Numeric

      String

      List and vector

      Data frame

      Linear regression

      Python Reference

      Introduction

      Python Hello World example

      Comments

      Data types

      Int

      Float

      String

      Tuple

      List

      Set

      Dictionary

      Flow control

      For loop

      For loop on range

      For loop on list

      Break and continue

      Functions

      Program arguments

      Reading and writing the file

      Glossary of Algorithms and Methods in Data Science

      Preface

      Data science is a discipline at the intersection of machine learning, statistics and data mining with the objective to gain new knowledge from the existing data by the means of algorithmic and statistical analysis. In this book you will learn the 7 most important ways in Data Science to analyze 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 the knowledge of a particular analysis.

      What this book covers

      Chapter 1, Classification Using K Nearest Neighbors, Classify a data item based on the k most similar items.

      Chapter 2, Naive Bayes, Learn Bayes Theorem to compute the probability a data item belonging to a certain class.

      Chapter 3, Decision Trees, Organize your decision criteria into the branches of a tree and use a decision tree to classify a data item into one of the classes at the leaf node.

      Chapter 4, Random Forest, Classify 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, Divide your data into k clusters to discover the patterns and similarities between the data items. Exploit these patterns to classify new data.

      Chapter 6, Regression, Model a phenomena in your data by a function that can predict the values for the unknown data in a simple way.

      Chapter 7, Time Series Analysis, Unveil the trend and repeating patters in time dependent data to predict the future of the stock market, Bitcoin prices and other time events.

      Appendix A, Statistics, Provides a summary of the statistical methods and tools useful to a data scientist.

      Appendix B, R Reference, Reference to the basic Python language constructs.

      Appendix C, Python Reference, Reference to the basic R language constructs, commands and functions used throughout the book.

      Appendix D, Glossary of Algorithms and Methods in Data Science, Provides a glossary for some of the most important and powerful algorithms and methods from the fields of the data science and machine learning.

      What you need for this book

      Most importantly, an active attitude to think of the problems--a lot of new content is presented in the exercises. Then you also need to be able to run Python and R programs under the operating system of your choice. The author ran the programs under Linux operating system using command line.

      Who this book is for

      This book is for aspiring data science professionals who are familiar with Python & R and have some statistics background. Those developers who are currently implementing 1 or 2 data science algorithms and now want to learn more to expand their skill will find this book quite useful.

      Conventions

      In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning. Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: For the visualization depicted earlier in this chapter, the matplotlib library was used.

      A block of code is set as follows:

      import sys

      sys.path.append('..')

      sys.path.append('../../common')

      import knn # noqa

      import common # noqa

      Any command-line input or output is written as follows:

      $ python knn_to_data.py mary_and_temperature_preferences.data mary_and_temperature_preferences_completed.data 1 5 30 0 10

      New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: In order to download new modules, we will go to Files | Settings | Project Name | Project Interpreter.

      Warnings or important notes appear like this.

      Tips and tricks appear like this.

      Reader feedback

      Feedback from our readers is always welcome. Let us know what you think about this book-what you liked or disliked. Reader feedback is important for us as it helps us develop titles that you will really get the most out of. To send us general feedback, simply e-mail [email protected], and mention the book's title in the subject of your message. If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, see our author guide at www.packtpub.com/authors.

      Customer support

      Now that you are the proud owner of a Packt book,

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