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Overview
You don't need a time machine to predict the future. All it takes is a little knowledge and know-how, and Predictive Analytics For Dummies gets you there fast. With the help of this friendly guide, you'll discover the core of predictive analytics and get started putting it to use with readily available tools to collect and analyze data. In no time, you'll learn how to incorporate algorithms through data models, identify similarities and relationships in your data, and predict the future through data classification. Along the way, you'll develop a roadmap by preparing your data, creating goals, processing your data, and building a predictive model that will get you stakeholder buy-in.
Big Data has taken the marketplace by storm, and companies are seeking qualified talent to quickly fill positions to analyze the massive amount of data that are being collected each day. If you want to get in on the action and either learn or deepen your understanding of how to use predictive analytics to find real relationships between what you know and what you want to know, everything you need is a page away!
- Offers common use cases to help you get started
- Covers details on modeling, k-means clustering, and more
- Includes information on structuring your data
- Provides tips on outlining business goals and approaches
The future starts today with the help of Predictive Analytics For Dummies.
Product Details
| ISBN-13: | 9781119267003 |
|---|---|
| Publisher: | Wiley |
| Publication date: | 10/31/2016 |
| Series: | For Dummies Books |
| Edition description: | 2nd ed. |
| Pages: | 464 |
| Sales rank: | 1,080,714 |
| Product dimensions: | 7.30(w) x 9.40(h) x 0.90(d) |
About the Author
Mohamed Chaouchi is a veteran software engineer who has conducted extensive research using data mining methods.
Tommy Jung is a software engineer with expertise in enterprise web applications and analytics.
Read an Excerpt
Predictive Analytics For Dummies
By Anasse Bari, Mohamed Chaouchi, Tommy Jung
John Wiley & Sons
Copyright © 2014 John Wiley & Sons, LtdAll rights reserved.
ISBN: 978-1-118-72896-3
CHAPTER 1
Entering the Arena
In This Chapter
* Explaining the building blocks
* Probing capabilities
* Surveying the market
Predictive analytics is a bright light bulb powered by your data.
You can never have too much insight. The more you see, the better the decisions you make — and you never want to be in the dark. You want to see what lies ahead, preferably before others do. It's like playing the game "Let's Make a Deal" where you have to choose the door with the hidden prize. Which door do you choose? Door 1, Door 2, or Door 3? They all look the same, so it's just your best guess — your choice depends on you and your luck. But what if you had an edge — the ability to see through the keyhole? Predictive analytics can give you that edge.
Exploring Predictive Analytics
What would you do in a world where you know how likely you are to end up marrying your college roommate? Where you can predict what profession will best suit you? Where you can predict the best city and country for you to live in?
In short, imagine a world where you can maximize the potential of every moment of your life. Such a life would be productive, efficient, and powerful. You will (in effect) have superpowers — and a lot more spare time. Well, such a world may seem a little boring to people who like to take uncalculated risks, but not to a profit-generating organization. Organizations spend millions of dollars managing risk. And if there is something out there that helps them manage their risk, optimize their operations, and maximize their profits, you should definitely learn about it. That is the world of predictive analytics.
Mining data
Big data is the new reality. In fact, data is only getting bigger, faster, and richer. It's here to stay and you'd better capitalize on it.
Data is one of your organization's most valuable assets. It's full of hidden value, but you have to dig for it. Data mining is the discovery of hidden patterns of data through machine learning — and sophisticated algorithms are the mining tools. Predictive analytics is the process of refining that data resource, using business knowledge to extract hidden value from those newly discovered patterns.
Data mining + business knowledge = predictive analytics = value
Today's leading organizations are looking at their data, examining it, and processing it to search for ways to better understand their customer base, improve their operations, outperform their competitors, and better position themselves in the marketplace. They are looking into how they can use that information to increase their market share and sharpen their competitive edge. How can they drive better sales and more effectively targeted marketing campaigns? How can they better serve their customers and meet their needs? What can they do to improve the bottom line?
But these tools are useful in realms beyond business. As one major example, government law enforcement agencies are asking questions related to crime detection and prevention. Is this a person of interest? Is this person about to commit a heinous crime? Will this criminal be a repeat offender? Where will the next crime happen?
Other industries, notably those with financial responsibility, could use a trustworthy glimpse into the future. Companies are trying to know ahead of time whether the transaction they're currently processing is fraudulent, whether an insurance claim is legitimate, whether a credit card purchase is valid, whether a credit applicant is worthy of credit ... the list goes on.
Governments, companies, and individuals are (variously) looking to spot trends in social movements, detect emerging healthcare issues and disease outbreaks, uncover new fashion trends, or find that perfect lifetime partner.
These — and plenty more — business and research questions are topics you can investigate further to find answers to by mining the available data and building predictive analytics models to guide future decisions.
Data + predictive analytics = light.
Highlighting the model
A model is a mathematical representation of an object or a process. We build models to simulate real-world phenomena as a further investigative step, in hopes of understanding more clearly what's really going on. For example, to model our customers' behavior, we seek to mimic how our customers have been navigating through our websites:
[check] What products did they look at before they made a purchase?
[check] What pages did they view before making that purchase?
[check] Did they look at the products' descriptions?
[check] Did they read users' reviews?
[check] How many reviews did they read?
[check] Did they read both positive and negative reviews?
[check] Did they purchase something else in addition to the product they came looking for?
We collect all that data from past occurrences. We look at those historical transactions between our company and our customers — and try to make consistent sense of them. We examine that data and see whether it holds answers to our questions. Collecting that data — with particular attention to the breadth and depth of the data, its quality level, and its predictive value — helps to form the boundaries that will define our model and its outputs.
This process is not to be confused with just reporting on the data; it's also different from just visualizing that data. Although those steps are vital, they're just the beginning of exploring the data and gaining a usable understanding of it.
We go a lot deeper when we're talking about developing predictive analytics. In the first place, we need to take a threefold approach:
[check] Thoroughly understand the business problem we're trying to solve.
[check] Obtain and prepare the data we want our model to work with.
[check] Run statistical analysis, data-mining, and machine-learning algorithms on the data.
In the process, we have to look at various attributes — data points we think are relevant to our analysis. We'll run several algorithms, which are sets of mathematical instructions that get machines to do problem-solving.
We keep running through possible combinations of data and investigate what-if scenarios. Eventually we find our answers, build our model, and prepare to deploy it and reap its benefits.
What does a model look like? Well, in programming terms, a predictive analytics model can be as simple as a few if ... then statements that tell the machine, "If this condition exists, then perform this action."
Here are some simple rule-based trading models:
[check] If it's past 10:00a.m. ET and the market is up, then buy 100 shares of XYZ stock.
[check] If my stock is up by 10 percent, then take profits.
[check] If my portfolio is down by 10 percent, then exit my positions.
Here's a simple rule-based recommender system (for more about recommender systems, see Chapter 2):
[check] If a person buys a book by this author, then recommend other books by the same author.
[check] If a person buys a book on this topic, then recommend other books on the same and related topic.
[check] If a person buys a book on this topic, then recommend books that other customers have purchased when they bought this book.
Adding Business Value
In an increasingly competitive environment, organizations always need ways to become more competitive. Predictive analytics found its way into organizations as one such tool. Using technology in the form of machine-learning algorithms, statistics, and data-mining techniques, organizations can uncover hidden patterns and trends in their data that can aid in operations and strategy and help fulfill critical business needs.
Embedding predictive analytics in operational decisions improves return on investment because organizations spend less time dealing with low-impact, low-risk operational decisions. Employees can focus more of their time on high-impact, high-risk decisions. For instance, most standard insurance claims can be automatically paid out. However, if the predictive model comes across a claim that's unusual (an outlier), or if the claim exhibits the same pattern as a fraudulent claim, the system can flag the claim automatically and send it to the appropriate person to take action.
By using predictive analytics to predict a future event or trend, the company can create a strategy to position itself to take advantage of that insight. If your predictive model is telling you (for example) that the trend in fashion is toward black turtlenecks, you can take appropriate actions to design more black-colored turtlenecks or design more accessories to go with the fashionable item.
Endless opportunities
Organizations around the world are striving to improve, compete, and be lean. They're looking to make their planning process more agile. They're investigating how to manage inventories and optimize the allocations of their human resources to best advantage. They're looking to act on opportunities as they arise in real time.
Predictive analytics can make all those goals more reachable. The domains to which predictive analytics can be applied are unlimited; the arena is wide open and everything is fair game. Let the mining start. Let the analysis begin.
Go to your analytics team and have them mine the data you've accumulated or acquired, with an eye toward finding an advantageous niche market for your product; innovate with data. Ask the team to help you gain confidence in your decision-making and risk management.
Albert Einstein once said, "Know where to find information and how to use it; that is the secret of success." If that's the secret to success, then you will succeed by using predictive analytics: The information is in your data and data mining will find it. The rest of the equation relies on your business knowledge of how to interpret that information — and ultimately use it to create success.
Finding value in data equals success. Therefore we can rewrite our predictive analytics equation as
Data mining + business knowledge = predictive analytics = success
Empowering your organization
Predictive analytics empowers your organization by providing three advantages:
[check] Vision
[check] Decision
[check] Precision
Vision
Predictive analytics will lead you to see what is invisible to others — in particular, useful patterns in your data.
Predictive analytics can provide you with powerful hints to lend direction to the decisions you're about to make in your company's quest to retain customers, attract more customers, and maximize profits. Predictive analytics can go through a lot of past customer data, associate it with other pieces of data, and assemble all the pieces in the right order to solve that puzzle in various ways, including
[check] Categorizing your customers and speculate about their needs.
[check] Knowing your customers' wish lists.
[check] Guessing your customers' next actions.
[check] Categorizing your customers as loyal, seasonal, or wandering.
Knowing this type of information beforehand shapes your strategic planning and helps optimize resource allocation, increase customer satisfaction, and maximize your profits.
Decision
A well-made predictive analytics model provides analytical results free of emotion and bias. The model uses mathematical functions to derive forward insights from numbers and text that describe past facts and current information. The model provides you with consistent and unbiased insights to support your decisions.
Consider the scenario of a typical application for a credit card: The process takes a few minutes; the bank or agency makes a quick, fact-based decision on whether to extend credit, and is confident in their decision. The speed of that transaction is possible thanks to predictive analytics, which predicted the applicant's creditworthiness.
Precision
Imagine having to read a lot of reports, derive insights from the past facts buried in them, go through rows of Excel spreadsheets to compare results, or extract information from a large array of numbers. You'd need a staff to do these time-consuming tasks. With predictive analytics, you can use automated tools to do the job for you — saving time and resources, reduces human error, and improves precision.
For example, you can focus targeted marketing campaigns by examining the data you have about your customers, their demographics, and their purchases. When you know precisely which customers you should market to, you can zero in on those most likely to buy.
Starting a Predictive Analytic Project
For the moment, let's forget about algorithms and higher math; predictions are used in every aspect of our lives. Consider how many times you have said (or heard people say), "I told you that was going to happen."
If you want to predict a future event with any accuracy, however, you'll need to know the past and understand the current situation. Doing so entails several processes:
[check] Extract the facts that are currently happening.
[check] Distinguish present facts from those that just happened.
[check] Derive possible scenarios that could happen.
[check] Rank the scenarios according to how likely they are to happen.
Predictive analytics can help you with each of these processes, so that you know as much as you can about what has happened and can make better-informed decisions about the future.
Companies typically create predictive analytics solutions by combining three ingredients:
[check] Business knowledge
[check] Data-science team and technology
[check] The data
Though the proportion of the three ingredients will vary from one business to the next, all are required for a successful predictive analytic solution that yields actionable insights.
Business knowledge
Because any predictive analytics project is started to fulfill a business need, business-specific knowledge and a clear business objective are critical to its success. Ideas for a project can come from anyone within the organization, but it's up to the leadership team to set the business goals and get buy-in from the needed departments across the whole organization.
Be sure the decision-makers in your team are prepared to act. When you present a prototype of your project, it needs an in-house champion — someone who's going to push for its adoption.
The leadership team or domain experts must also set clear metrics — ways to quantify and measure the outcome of the project. Appropriate metrics keep the departments involved are clear about what they need to do, how much they need to do, and whether what they're doing is helping the company achieve its business goals.
The business stakeholders are those who are most familiar with the domain of the business. They'll have ideas about which correlations — relationships between features — of data work and which don't, which variables are important to the model, and whether you should create new variables — as in derived features or attributes — to improve the model.
Business analysts and other domain experts can analyze and interpret the patterns discovered by the machines, making useful meaning out of the data patterns and deriving actionable insights.
This is an iterative (building a model and interpreting its findings) process between business and science. In the course of building a predictive model, you have to try successive versions of the model to improve how it works (which is what data experts mean when they say iterate the model over its lifecycle). You might go through a lot of revisions and repetitions before you can prove that your model is bringing real value to the business. Even after the predictive models are deployed, the business must monitor the results, validate the accuracy of the models and improve upon the models as more data is being collected.
(Continues...)
Excerpted from Predictive Analytics For Dummies by Anasse Bari, Mohamed Chaouchi, Tommy Jung. Copyright © 2014 John Wiley & Sons, Ltd. Excerpted by permission of John Wiley & Sons.
All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.
Table of Contents
INTRODUCTION 1PART 1: GETTING STARTED WITH PREDICTIVE ANALYTICS 5
CHAPTER 1: Entering the Arena 7
Exploring Predictive Analytics 7
Mining data 8
Highlighting the model 9
Adding Business Value 10
Endless opportunities 11
Empowering your organization 12
Starting a Predictive Analytic Project 13
Business knowledge 14
Data-science team and technology 15
The Data 16
Ongoing Predictive Analytics 17
Forming Your Predictive Analytics Team 18
Hiring experienced practitioners 18
Demonstrating commitment and curiosity 19
Surveying the Marketplace 19
Responding to big data 20
Working with big data 20
CHAPTER 2: Predictive Analytics in the Wild 23
Online Marketing and Retail 25
Recommender systems 25
Personalized shopping on the Internet 26
Implementing a Recommender System 28
Collaborative filtering 28
Content-based filtering 36
Hybrid recommender systems 39
Target Marketing 41
Targeting using predictive modeling 42
Uplift modeling 43
Personalization 46
Online customer experience 46
Retargeting 47
Implementation 47
Optimizing using personalization 48
Similarities of Personalization and Recommendations 48
Content and Text Analytics 50
CHAPTER 3: Exploring Your Data Types and Associated Techniques 51
Recognizing Your Data Types 52
Structured and unstructured data 52
Static and streamed data 56
Identifying Data Categories 58
Attitudinal data 59
Behavioral data 60
Demographic data 61
Generating Predictive Analytics 61
Data-driven analytics 62
User-driven analytics 64
Connecting to Related Disciplines 65
Statistics 65
Data mining 66
Machine learning 67
CHAPTER 4: Complexities of Data 69
Finding Value in Your Data 70
Delving into your data 70
Data validity 70
Data variety 71
Constantly Changing Data 72
Data velocity 72
High volume of data 73
Complexities in Searching Your Data 73
Keyword-based search 74
Semantic-based search 74
Contextual search 76
Differentiating Business Intelligence from Big-Data Analytics 79
Exploration of Raw Data 80
Identifying data attributes 80
Exploring common data visualizations 81
Tabular visualizations 81
Word clouds 82
Flocking birds as a novel data representation 83
Graph charts 85
Common visualizations 87
PART 2: INCORPORATING ALGORITHMS IN YOUR MODELS 89
CHAPTER 5: Applying Models 91
Modeling Data 92
Models and simulation 92
Categorizing models 94
Describing and summarizing data 96
Making better business decisions 97
Healthcare Analytics Case Studies 97
Google Flu Trends 97
Cancer survivability predictors 99
Social and Marketing Analytics Case Studies 101
Target store predicts pregnant women 101
Twitter-based predictors of earthquakes 102
Twitter-based predictors of political campaign outcomes 103
Tweets as predictors for the stock market 105
Predicting variation of stock prices from news articles 106
Analyzing New York City’s bicycle usage 107
Predictions and responses 110
Data compression 111
Prognostics and its Relation to Predictive Analytics 112
The Rise of Open Data 113
CHAPTER 6: Identifying Similarities in Data 115
Explaining Data Clustering 116
Converting Raw Data into a Matrix 120
Creating a matrix of terms in documents 120
Term selection 121
Identifying Groups in Your Data 122
K-means clustering algorithm 122
Clustering by nearest neighbors 126
Density-based algorithms 130
Finding Associations in Data Items 132
Applying Biologically Inspired Clustering Techniques 136
Birds flocking: Flock by Leader algorithm 136
Ant colonies 143
CHAPTER 7: Predicting the Future Using Data Classification 147
Explaining Data Classification 149
Introducing Data Classification to Your Business 152
Exploring the Data-Classification Process 154
Using Data Classification to Predict the Future 156
Decision trees 156
Algorithms for Generating Decision Trees 159
Support vector machine 163
Ensemble Methods to Boost Prediction Accuracy 165
Naïve Bayes classification algorithm 166
The Markov Model 172
Linear regression 177
Neural networks 177
Deep Learning 179
PART 3: DEVELOPING A ROADMAP 185
CHAPTER 8: Convincing Your Management to Adopt Predictive Analytics 187
Making the Business Case 188
Gathering Support from Stakeholders 195
Presenting Your Proposal 206
CHAPTER 9: Preparing Data 209
Listing the Business Objectives 210
Processing Your Data 212
Identifying the data 212
Cleaning the data 213
Generating any derived data 215
Reducing the dimensionality of your data 215
Applying principal component analysis 216
Leveraging singular value decomposition 218
Working with Features 219
Structuring Your Data 224
Extracting, transforming and loading your data 225
Keeping the data up to date 226
Outlining testing and test data 226
CHAPTER 10: Building a Predictive Model 229
Getting Started 230
Defining your business objectives 232
Preparing your data 233
Choosing an algorithm 236
Developing and Testing the Model 237
Going Live with the Model 242
CHAPTER 11: Visualization of Analytical Results 245
Visualization as a Predictive Tool 246
Evaluating Your Visualization 249
Visualizing Your Model’s Analytical Results 251
Visualizing hidden groupings in your data 251
Visualizing data classification results 252
Visualizing outliers in your data 254
Visualization of Decision Trees 254
Visualizing predictions 256
Novel Visualization in Predictive Analytics 258
Big Data Visualization Tools 262
Tableau 263
Google Charts 263
Plotly 263
Infogram 264
PART 4: PROGRAMMING PREDICTIVE ANALYTICS 265
CHAPTER 12: Creating Basic Prediction Examples 267
Installing the Software Packages 268
Installing Python 268
Installing the machine-learning module 270
Installing the dependencies 274
Preparing the Data 278
Making Predictions Using Classification Algorithms 280
Creating a supervised learning model with SVM 281
Creating a supervised learning model with logistic regression 288
Creating a supervised learning model with random forest 295
Comparing the classification models 297
CHAPTER 13: Creating Basic Examples of Unsupervised Predictions 299
Getting the Sample Dataset 300
Using Clustering Algorithms to Make Predictions 301
Comparing clustering models 301
Creating an unsupervised learning model with K-means 302
Creating an unsupervised learning model with DBSCAN 314
Creating an unsupervised learning model with mean shift 318
CHAPTER 14: Predictive Modeling with R 323
Programming in R 325
Installing R 325
Installing RStudio 326
Getting familiar with the environment 327
Learning just a bit of R 328
Making Predictions Using R 334
Predicting using regression 334
Using classification to predict 345
Classification by random forest 354
CHAPTER 15: Avoiding Analysis Traps 359
Data Challenges 360
Outlining the limitations of the data 361
Dealing with extreme cases (outliers) 364
Data smoothing 367
Curve fitting 371
Keeping the assumptions to a minimum 374
Analysis Challenges 375
PART 5: EXECUTING BIG DATA 381
CHAPTER 16: Targeting Big Data 383
Major Technological Trends in Predictive Analytics 384
Exploring predictive analytics as a service 384
Aggregating distributed data for analysis 385
Real-time data-driven analytics 387
Applying Open-Source Tools to Big Data 388
Apache Hadoop 388
Apache Spark 394
CHAPTER 17: Getting Ready for Enterprise Analytics 399
Analytics as a Service 403
Google Analytics 403
IBM Watson 405
Microsoft Revolution R Enterprise 405
Preparing for a Proof-of-Value of Predictive Analytics Prototype 406
Prototyping for predictive analytics 406
Testing your predictive analytics model 409
PART 6: THE PART OF TENS 411
CHAPTER 18: Ten Reasons to Implement
Predictive Analytics 413
CHAPTER 19: Ten Steps to Build a Predictive Analytic Model 423
INDEX 433