Use Big Data and technology to uncover real-world insights 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.
, Mohamed Chaouchi
, Tommy Jung
John Wiley & Sons Inc
Country of Publication:
21 October 2016
Professional and scholarly
INTRODUCTION 1 PART 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 Naive 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
Anasse Bari, Ph.D. is data science expert and a university professor who has many years of predictive modeling and data analytics experience. 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.