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Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst by Abbott, Dean (2014) Paperback
Learn the art and science of predictive analytics ― techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included. Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.
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Paperback
First published January 1, 2014
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Displaying 1 - 3 of 3 reviews
May 9, 2015
Very useful and readable overview of machine learning/data mining/predictive analytics or whatever you want to call it.
Especially useful are the sections on data understanding and data preparation, which frankly is where you are going to be spending about 80% of your time anyway. You'll be trying to jam data into a uniform or normal distribution and maybe chopping off the outliers. It's not pretty being a data janitor, I mean data scientist.
There is also a brief overview of text techniques with a quick sentiment analysis example that is instructive.
Then it ends with tips on deploying a model and a walk through of several examples.
Great overview that describes features without getting bogged down in too much detail. I especially appreciates the overview of neural networks and their recent comeback. However it was odd that there was no mention of Hadoop, Spark, etc.
Recommended. Probably the best and quickest overview and intro.
Especially useful are the sections on data understanding and data preparation, which frankly is where you are going to be spending about 80% of your time anyway. You'll be trying to jam data into a uniform or normal distribution and maybe chopping off the outliers. It's not pretty being a data janitor, I mean data scientist.
There is also a brief overview of text techniques with a quick sentiment analysis example that is instructive.
Then it ends with tips on deploying a model and a walk through of several examples.
Great overview that describes features without getting bogged down in too much detail. I especially appreciates the overview of neural networks and their recent comeback. However it was odd that there was no mention of Hadoop, Spark, etc.
Recommended. Probably the best and quickest overview and intro.
September 27, 2023
It's perhaps weird to give 5 stars to a 10 year old machine learning book, considering the recent advancements in AI and the fact that even books published in early 2023 could be obsolete by now.
BUT - this book did a great job to explain predictive analytics as a "whole thing" - from agreeing a business goal to deploying and monitoring a live model.
It mostly focuses on classic predictive analytics that takes a business problem that a team feels can be solved by "clustering" or "scoring" or "forecasting", takes a bunch of data and then tries to build a model that outputs said clusters, scores or forecasts.
The strengths:
1. The author is clearly a practitioner (as opposed to mostly theoretical model builder). Meaning - they take a holistic view - how to approach business problems, what considerations are for data, how to do the nitty-gritty work of data cleaning and normalizing, how to build AND deploy a model and measure it's effectiveness.
2. Ample space is dedicated to picking and cleaning data as well as operational considerations. This is perhaps the least sexy part of analytics, but can easily be the most tricky bit to get right and take 90%+ of the project effort. The book goes into intimate dirty details of missing values, normalizing distributions, eliminating outliers, etc. Very useful. Best part of the book and I learned quite a bit.
3. Predictive analytics techniques and models are described as tools, not just algorithms - what inputs do they expect, what levers can you pull, how to interpret results. What are the common pitfalls.
The weak parts of the book were mostly due to it's age:
1. It's hopelessly behind on the modern "AI" stuff - the paragraph on text mining is by now ancient history
2. It's written from a consultancy standpoint and the process feels more old-school and waterfally than the "cross-functional", "dev-ops", "fast iterations" product building that happens in modern tech companies.
Great book to understand the basics of predictive analytics, though. Even now when ChatGPT exists
BUT - this book did a great job to explain predictive analytics as a "whole thing" - from agreeing a business goal to deploying and monitoring a live model.
It mostly focuses on classic predictive analytics that takes a business problem that a team feels can be solved by "clustering" or "scoring" or "forecasting", takes a bunch of data and then tries to build a model that outputs said clusters, scores or forecasts.
The strengths:
1. The author is clearly a practitioner (as opposed to mostly theoretical model builder). Meaning - they take a holistic view - how to approach business problems, what considerations are for data, how to do the nitty-gritty work of data cleaning and normalizing, how to build AND deploy a model and measure it's effectiveness.
2. Ample space is dedicated to picking and cleaning data as well as operational considerations. This is perhaps the least sexy part of analytics, but can easily be the most tricky bit to get right and take 90%+ of the project effort. The book goes into intimate dirty details of missing values, normalizing distributions, eliminating outliers, etc. Very useful. Best part of the book and I learned quite a bit.
3. Predictive analytics techniques and models are described as tools, not just algorithms - what inputs do they expect, what levers can you pull, how to interpret results. What are the common pitfalls.
The weak parts of the book were mostly due to it's age:
1. It's hopelessly behind on the modern "AI" stuff - the paragraph on text mining is by now ancient history
2. It's written from a consultancy standpoint and the process feels more old-school and waterfally than the "cross-functional", "dev-ops", "fast iterations" product building that happens in modern tech companies.
Great book to understand the basics of predictive analytics, though. Even now when ChatGPT exists
December 22, 2014
In recent years, the data collected by computer applications has grown significantly both in volume and diversity. Data owners are keen to derive value from these data sets by discovering patterns and relationships within them and by using them to generate predictions that can be employed in variety of ways.
This book takes a process-level view of predictive analytics programmes.
The content, which is aligned to the Cross-Industry Standard Process for Data Mining (CRISP-DM), provides a practical description, founded on the author’s own experience, of each of the six stages of CRISP-DM, which together take a predictive analytics programme from initial business requirements gathering through to final deployment.
After an introductory chapter, which outlines key concepts relating to predictive analytics, the book moves on to discuss the definition and initiation of a predictive analytics programme, including the identification of business objectives, source data and evaluation criteria.
The author describes a variety of exploratory data analysis techniques, including the use of data visualisation approaches, that enable practitioners to familiarise themselves with the available data sets and their relationships to the business context. The author also describes a variety of unsupervised learning techniques as a means to derive descriptive models, which support further exploration of source data.
Recognising that the data sets used in analytics programmes are often incomplete, an extensive chapter of the book is devoted to data preparation. Here, the author explores some of the key challenges that can be posed by real-world data sets and describes techniques that can be used to identify and handle incorrect or missing data, reduce data size and select appropriate data subsets for model training.
As may be expected, a significant part of the book is devoted to the description of supervised predictive analytics approaches including practical advice on the selection and evaluation of algorithms.
In contrast to some books in this field, which focus on the mathematical and statistical inner workings of algorithms, this book explains concepts in plain English and treats analytics algorithms more in terms of ‘black boxes’ with particular characteristics, which make them more or less suitable for different data sets and applications.
This is supplemented by a chapter on the use of multiple models, “model ensembles”, as a means to improve model accuracy, as well as a chapter devoted to mining data from unstructured text sources.
Having covered the process of building and evaluating suitable predictive models, the author provides a useful chapter that describes some of the practical issues, especially those of a non-technical nature, that may be encountered when deploying the models.
This book provides an excellent background to predictive analytics and should appeal to a broad readership. The writing style is authoritative, and while occasionally jumping ahead or assuming knowledge that the book has yet to cover, is generally easy to read. Real-world examples are used throughout, and while the book does not refer to any particular analytics software, the data for these examples is available for download from the book’s website, enabling readers to explore the topics using their software of choice.
Reviewed by Dr Patrick Hill CEng MBCS CITP
This book takes a process-level view of predictive analytics programmes.
The content, which is aligned to the Cross-Industry Standard Process for Data Mining (CRISP-DM), provides a practical description, founded on the author’s own experience, of each of the six stages of CRISP-DM, which together take a predictive analytics programme from initial business requirements gathering through to final deployment.
After an introductory chapter, which outlines key concepts relating to predictive analytics, the book moves on to discuss the definition and initiation of a predictive analytics programme, including the identification of business objectives, source data and evaluation criteria.
The author describes a variety of exploratory data analysis techniques, including the use of data visualisation approaches, that enable practitioners to familiarise themselves with the available data sets and their relationships to the business context. The author also describes a variety of unsupervised learning techniques as a means to derive descriptive models, which support further exploration of source data.
Recognising that the data sets used in analytics programmes are often incomplete, an extensive chapter of the book is devoted to data preparation. Here, the author explores some of the key challenges that can be posed by real-world data sets and describes techniques that can be used to identify and handle incorrect or missing data, reduce data size and select appropriate data subsets for model training.
As may be expected, a significant part of the book is devoted to the description of supervised predictive analytics approaches including practical advice on the selection and evaluation of algorithms.
In contrast to some books in this field, which focus on the mathematical and statistical inner workings of algorithms, this book explains concepts in plain English and treats analytics algorithms more in terms of ‘black boxes’ with particular characteristics, which make them more or less suitable for different data sets and applications.
This is supplemented by a chapter on the use of multiple models, “model ensembles”, as a means to improve model accuracy, as well as a chapter devoted to mining data from unstructured text sources.
Having covered the process of building and evaluating suitable predictive models, the author provides a useful chapter that describes some of the practical issues, especially those of a non-technical nature, that may be encountered when deploying the models.
This book provides an excellent background to predictive analytics and should appeal to a broad readership. The writing style is authoritative, and while occasionally jumping ahead or assuming knowledge that the book has yet to cover, is generally easy to read. Real-world examples are used throughout, and while the book does not refer to any particular analytics software, the data for these examples is available for download from the book’s website, enabling readers to explore the topics using their software of choice.
Reviewed by Dr Patrick Hill CEng MBCS CITP
Displaying 1 - 3 of 3 reviews