{"product_id":"predictive-analytics-for-dummies-isbn-9781119267003","title":"Predictive Analytics For Dummies","description":"\u003cb\u003eUse Big Data and technology to uncover real-world insights\u003c\/b\u003e \u003cp\u003eYou don't need a time machine to predict the future. All it takes is a little knowledge and know-how, and \u003ci\u003ePredictive Analytics For Dummies\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eBig 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! \u003c\/p\u003e\u003cul\u003e \u003cli\u003eOffers common use cases to help you get started\u003c\/li\u003e \u003cli\u003eCovers details on modeling, k-means clustering, and more\u003c\/li\u003e \u003cli\u003eIncludes information on structuring your data\u003c\/li\u003e \u003cli\u003eProvides tips on outlining business goals and approaches\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe future starts today with the help of \u003ci\u003ePredictive Analytics For Dummies\u003c\/i\u003e. \u003c\/p\u003e\u003cp\u003eINTRODUCTION 1\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART 1: GETTING STARTED WITH PREDICTIVE ANALYTICS 5\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 1: Entering the Arena 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExploring Predictive Analytics 7\u003c\/p\u003e \u003cp\u003eMining data 8\u003c\/p\u003e \u003cp\u003eHighlighting the model 9\u003c\/p\u003e \u003cp\u003eAdding Business Value 10\u003c\/p\u003e \u003cp\u003eEndless opportunities 11\u003c\/p\u003e \u003cp\u003eEmpowering your organization 12\u003c\/p\u003e \u003cp\u003eStarting a Predictive Analytic Project 13\u003c\/p\u003e \u003cp\u003eBusiness knowledge 14\u003c\/p\u003e \u003cp\u003eData-science team and technology 15\u003c\/p\u003e \u003cp\u003eThe Data 16\u003c\/p\u003e \u003cp\u003eOngoing Predictive Analytics 17\u003c\/p\u003e \u003cp\u003eForming Your Predictive Analytics Team 18\u003c\/p\u003e \u003cp\u003eHiring experienced practitioners 18\u003c\/p\u003e \u003cp\u003eDemonstrating commitment and curiosity 19\u003c\/p\u003e \u003cp\u003eSurveying the Marketplace 19\u003c\/p\u003e \u003cp\u003eResponding to big data 20\u003c\/p\u003e \u003cp\u003eWorking with big data 20\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 2: Predictive Analytics in the Wild 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOnline Marketing and Retail 25\u003c\/p\u003e \u003cp\u003eRecommender systems 25\u003c\/p\u003e \u003cp\u003ePersonalized shopping on the Internet 26\u003c\/p\u003e \u003cp\u003eImplementing a Recommender System 28\u003c\/p\u003e \u003cp\u003eCollaborative filtering 28\u003c\/p\u003e \u003cp\u003eContent-based filtering 36\u003c\/p\u003e \u003cp\u003eHybrid recommender systems 39\u003c\/p\u003e \u003cp\u003eTarget Marketing 41\u003c\/p\u003e \u003cp\u003eTargeting using predictive modeling 42\u003c\/p\u003e \u003cp\u003eUplift modeling 43\u003c\/p\u003e \u003cp\u003ePersonalization 46\u003c\/p\u003e \u003cp\u003eOnline customer experience 46\u003c\/p\u003e \u003cp\u003eRetargeting 47\u003c\/p\u003e \u003cp\u003eImplementation 47\u003c\/p\u003e \u003cp\u003eOptimizing using personalization 48\u003c\/p\u003e \u003cp\u003eSimilarities of Personalization and Recommendations 48\u003c\/p\u003e \u003cp\u003eContent and Text Analytics 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 3: Exploring Your Data Types and Associated Techniques 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRecognizing Your Data Types 52\u003c\/p\u003e \u003cp\u003eStructured and unstructured data 52\u003c\/p\u003e \u003cp\u003eStatic and streamed data 56\u003c\/p\u003e \u003cp\u003eIdentifying Data Categories 58\u003c\/p\u003e \u003cp\u003eAttitudinal data 59\u003c\/p\u003e \u003cp\u003eBehavioral data 60\u003c\/p\u003e \u003cp\u003eDemographic data 61\u003c\/p\u003e \u003cp\u003eGenerating Predictive Analytics 61\u003c\/p\u003e \u003cp\u003eData-driven analytics 62\u003c\/p\u003e \u003cp\u003eUser-driven analytics 64\u003c\/p\u003e \u003cp\u003eConnecting to Related Disciplines 65\u003c\/p\u003e \u003cp\u003eStatistics 65\u003c\/p\u003e \u003cp\u003eData mining 66\u003c\/p\u003e \u003cp\u003eMachine learning 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 4: Complexities of Data 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFinding Value in Your Data 70\u003c\/p\u003e \u003cp\u003eDelving into your data 70\u003c\/p\u003e \u003cp\u003eData validity 70\u003c\/p\u003e \u003cp\u003eData variety 71\u003c\/p\u003e \u003cp\u003eConstantly Changing Data 72\u003c\/p\u003e \u003cp\u003eData velocity 72\u003c\/p\u003e \u003cp\u003eHigh volume of data 73\u003c\/p\u003e \u003cp\u003eComplexities in Searching Your Data 73\u003c\/p\u003e \u003cp\u003eKeyword-based search 74\u003c\/p\u003e \u003cp\u003eSemantic-based search 74\u003c\/p\u003e \u003cp\u003eContextual search 76\u003c\/p\u003e \u003cp\u003eDifferentiating Business Intelligence from Big-Data Analytics 79\u003c\/p\u003e \u003cp\u003eExploration of Raw Data 80\u003c\/p\u003e \u003cp\u003eIdentifying data attributes 80\u003c\/p\u003e \u003cp\u003eExploring common data visualizations 81\u003c\/p\u003e \u003cp\u003eTabular visualizations 81\u003c\/p\u003e \u003cp\u003eWord clouds 82\u003c\/p\u003e \u003cp\u003eFlocking birds as a novel data representation 83\u003c\/p\u003e \u003cp\u003eGraph charts 85\u003c\/p\u003e \u003cp\u003eCommon visualizations 87\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART 2: INCORPORATING ALGORITHMS IN YOUR MODELS 89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 5: Applying Models 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eModeling Data 92\u003c\/p\u003e \u003cp\u003eModels and simulation 92\u003c\/p\u003e \u003cp\u003eCategorizing models 94\u003c\/p\u003e \u003cp\u003eDescribing and summarizing data 96\u003c\/p\u003e \u003cp\u003eMaking better business decisions 97\u003c\/p\u003e \u003cp\u003eHealthcare Analytics Case Studies 97\u003c\/p\u003e \u003cp\u003eGoogle Flu Trends 97\u003c\/p\u003e \u003cp\u003eCancer survivability predictors 99\u003c\/p\u003e \u003cp\u003eSocial and Marketing Analytics Case Studies 101\u003c\/p\u003e \u003cp\u003eTarget store predicts pregnant women 101\u003c\/p\u003e \u003cp\u003eTwitter-based predictors of earthquakes 102\u003c\/p\u003e \u003cp\u003eTwitter-based predictors of political campaign outcomes 103\u003c\/p\u003e \u003cp\u003eTweets as predictors for the stock market 105\u003c\/p\u003e \u003cp\u003ePredicting variation of stock prices from news articles 106\u003c\/p\u003e \u003cp\u003eAnalyzing New York City’s bicycle usage 107\u003c\/p\u003e \u003cp\u003ePredictions and responses 110\u003c\/p\u003e \u003cp\u003eData compression 111\u003c\/p\u003e \u003cp\u003ePrognostics and its Relation to Predictive Analytics 112\u003c\/p\u003e \u003cp\u003eThe Rise of Open Data 113\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 6: Identifying Similarities in Data 115\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExplaining Data Clustering 116\u003c\/p\u003e \u003cp\u003eConverting Raw Data into a Matrix 120\u003c\/p\u003e \u003cp\u003eCreating a matrix of terms in documents 120\u003c\/p\u003e \u003cp\u003eTerm selection 121\u003c\/p\u003e \u003cp\u003eIdentifying Groups in Your Data 122\u003c\/p\u003e \u003cp\u003eK-means clustering algorithm 122\u003c\/p\u003e \u003cp\u003eClustering by nearest neighbors 126\u003c\/p\u003e \u003cp\u003eDensity-based algorithms 130\u003c\/p\u003e \u003cp\u003eFinding Associations in Data Items 132\u003c\/p\u003e \u003cp\u003eApplying Biologically Inspired Clustering Techniques 136\u003c\/p\u003e \u003cp\u003eBirds flocking: Flock by Leader algorithm 136\u003c\/p\u003e \u003cp\u003eAnt colonies 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 7: Predicting the Future Using Data Classification 147\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExplaining Data Classification 149\u003c\/p\u003e \u003cp\u003eIntroducing Data Classification to Your Business 152\u003c\/p\u003e \u003cp\u003eExploring the Data-Classification Process 154\u003c\/p\u003e \u003cp\u003eUsing Data Classification to Predict the Future 156\u003c\/p\u003e \u003cp\u003eDecision trees 156\u003c\/p\u003e \u003cp\u003eAlgorithms for Generating Decision Trees 159\u003c\/p\u003e \u003cp\u003eSupport vector machine 163\u003c\/p\u003e \u003cp\u003eEnsemble Methods to Boost Prediction Accuracy 165\u003c\/p\u003e \u003cp\u003eNaïve Bayes classification algorithm 166\u003c\/p\u003e \u003cp\u003eThe Markov Model 172\u003c\/p\u003e \u003cp\u003eLinear regression 177\u003c\/p\u003e \u003cp\u003eNeural networks 177\u003c\/p\u003e \u003cp\u003eDeep Learning 179\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART 3: DEVELOPING A ROADMAP 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 8: Convincing Your Management to Adopt Predictive Analytics 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMaking the Business Case 188\u003c\/p\u003e \u003cp\u003eGathering Support from Stakeholders 195\u003c\/p\u003e \u003cp\u003ePresenting Your Proposal 206\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 9: Preparing Data 209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eListing the Business Objectives 210\u003c\/p\u003e \u003cp\u003eProcessing Your Data 212\u003c\/p\u003e \u003cp\u003eIdentifying the data 212\u003c\/p\u003e \u003cp\u003eCleaning the data 213\u003c\/p\u003e \u003cp\u003eGenerating any derived data 215\u003c\/p\u003e \u003cp\u003eReducing the dimensionality of your data 215\u003c\/p\u003e \u003cp\u003eApplying principal component analysis 216\u003c\/p\u003e \u003cp\u003eLeveraging singular value decomposition 218\u003c\/p\u003e \u003cp\u003eWorking with Features 219\u003c\/p\u003e \u003cp\u003eStructuring Your Data 224\u003c\/p\u003e \u003cp\u003eExtracting, transforming and loading your data 225\u003c\/p\u003e \u003cp\u003eKeeping the data up to date 226\u003c\/p\u003e \u003cp\u003eOutlining testing and test data 226\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 10: Building a Predictive Model 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting Started 230\u003c\/p\u003e \u003cp\u003eDefining your business objectives 232\u003c\/p\u003e \u003cp\u003ePreparing your data 233\u003c\/p\u003e \u003cp\u003eChoosing an algorithm 236\u003c\/p\u003e \u003cp\u003eDeveloping and Testing the Model 237\u003c\/p\u003e \u003cp\u003eGoing Live with the Model 242\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 11: Visualization of Analytical Results 245\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eVisualization as a Predictive Tool 246\u003c\/p\u003e \u003cp\u003eEvaluating Your Visualization 249\u003c\/p\u003e \u003cp\u003eVisualizing Your Model’s Analytical Results 251\u003c\/p\u003e \u003cp\u003eVisualizing hidden groupings in your data 251\u003c\/p\u003e \u003cp\u003eVisualizing data classification results 252\u003c\/p\u003e \u003cp\u003eVisualizing outliers in your data 254\u003c\/p\u003e \u003cp\u003eVisualization of Decision Trees 254\u003c\/p\u003e \u003cp\u003eVisualizing predictions 256\u003c\/p\u003e \u003cp\u003eNovel Visualization in Predictive Analytics 258\u003c\/p\u003e \u003cp\u003eBig Data Visualization Tools 262\u003c\/p\u003e \u003cp\u003eTableau 263\u003c\/p\u003e \u003cp\u003eGoogle Charts 263\u003c\/p\u003e \u003cp\u003ePlotly 263\u003c\/p\u003e \u003cp\u003eInfogram 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART 4: PROGRAMMING PREDICTIVE ANALYTICS 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 12: Creating Basic Prediction Examples 267\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eInstalling the Software Packages 268\u003c\/p\u003e \u003cp\u003eInstalling Python 268\u003c\/p\u003e \u003cp\u003eInstalling the machine-learning module 270\u003c\/p\u003e \u003cp\u003eInstalling the dependencies 274\u003c\/p\u003e \u003cp\u003ePreparing the Data 278\u003c\/p\u003e \u003cp\u003eMaking Predictions Using Classification Algorithms 280\u003c\/p\u003e \u003cp\u003eCreating a supervised learning model with SVM 281\u003c\/p\u003e \u003cp\u003eCreating a supervised learning model with logistic regression 288\u003c\/p\u003e \u003cp\u003eCreating a supervised learning model with random forest 295\u003c\/p\u003e \u003cp\u003eComparing the classification models 297\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 13: Creating Basic Examples of Unsupervised Predictions 299\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting the Sample Dataset 300\u003c\/p\u003e \u003cp\u003eUsing Clustering Algorithms to Make Predictions 301\u003c\/p\u003e \u003cp\u003eComparing clustering models 301\u003c\/p\u003e \u003cp\u003eCreating an unsupervised learning model with K-means 302\u003c\/p\u003e \u003cp\u003eCreating an unsupervised learning model with DBSCAN 314\u003c\/p\u003e \u003cp\u003eCreating an unsupervised learning model with mean shift 318\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 14: Predictive Modeling with R 323\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eProgramming in R 325\u003c\/p\u003e \u003cp\u003eInstalling R 325\u003c\/p\u003e \u003cp\u003eInstalling RStudio 326\u003c\/p\u003e \u003cp\u003eGetting familiar with the environment 327\u003c\/p\u003e \u003cp\u003eLearning just a bit of R 328\u003c\/p\u003e \u003cp\u003eMaking Predictions Using R 334\u003c\/p\u003e \u003cp\u003ePredicting using regression 334\u003c\/p\u003e \u003cp\u003eUsing classification to predict 345\u003c\/p\u003e \u003cp\u003eClassification by random forest 354\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 15: Avoiding Analysis Traps 359\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Challenges 360\u003c\/p\u003e \u003cp\u003eOutlining the limitations of the data 361\u003c\/p\u003e \u003cp\u003eDealing with extreme cases (outliers) 364\u003c\/p\u003e \u003cp\u003eData smoothing 367\u003c\/p\u003e \u003cp\u003eCurve fitting 371\u003c\/p\u003e \u003cp\u003eKeeping the assumptions to a minimum 374\u003c\/p\u003e \u003cp\u003eAnalysis Challenges 375\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART 5: EXECUTING BIG DATA 381\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 16: Targeting Big Data 383\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMajor Technological Trends in Predictive Analytics 384\u003c\/p\u003e \u003cp\u003eExploring predictive analytics as a service 384\u003c\/p\u003e \u003cp\u003eAggregating distributed data for analysis 385\u003c\/p\u003e \u003cp\u003eReal-time data-driven analytics 387\u003c\/p\u003e \u003cp\u003eApplying Open-Source Tools to Big Data 388\u003c\/p\u003e \u003cp\u003eApache Hadoop 388\u003c\/p\u003e \u003cp\u003eApache Spark 394\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 17: Getting Ready for Enterprise Analytics 399\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAnalytics as a Service 403\u003c\/p\u003e \u003cp\u003eGoogle Analytics 403\u003c\/p\u003e \u003cp\u003eIBM Watson 405\u003c\/p\u003e \u003cp\u003eMicrosoft Revolution R Enterprise 405\u003c\/p\u003e \u003cp\u003ePreparing for a Proof-of-Value of Predictive Analytics Prototype 406\u003c\/p\u003e \u003cp\u003ePrototyping for predictive analytics 406\u003c\/p\u003e \u003cp\u003eTesting your predictive analytics model 409\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART 6: THE PART OF TENS 411\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 18: Ten Reasons to Implement\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePredictive Analytics 413\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 19: Ten Steps to Build a Predictive Analytic Model 423\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eINDEX 433\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAnasse Bari, Ph.D. \u003c\/b\u003eis data science expert and a university professor who has many years of predictive modeling and data analytics experience.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMohamed Chaouchi \u003c\/b\u003eis a veteran software engineer who has conducted extensive research using data mining methods. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eTommy Jung\u003c\/b\u003e is a software engineer with expertise in enterprise web applications and analytics.  \u003c\/p\u003e\u003cp\u003eReal-world tips for creating business value\u003c\/p\u003e \u003cp\u003eDetails on modeling, data clustering, and more \u003c\/p\u003e\u003cp\u003eEnterprise use cases to help you get started \u003c\/p\u003e\u003cp\u003eLearn to \u003cb\u003epredict\u003c\/b\u003e the future! \u003c\/p\u003e\u003cp\u003eBusiness today relies on effectively using data to predict trends and sales. Predictive analytics is the tool that can make it happen, and this book eliminates the tricks and shows you how to use it. You'll learn to prepare and process your data, create goals, build a predictive model, get your organization's stakeholders on board, and more. \u003c\/p\u003e\u003cp\u003eInside... \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eHow to start a project\u003c\/li\u003e \u003cli\u003eIdentifying data types\u003c\/li\u003e \u003cli\u003eModeling tips\u003c\/li\u003e \u003cli\u003eWorking with algorithms\u003c\/li\u003e \u003cli\u003eHow data clustering works\u003c\/li\u003e \u003cli\u003eHow data classification works\u003c\/li\u003e \u003cli\u003eHow deep learning works\u003c\/li\u003e \u003cli\u003eAdvice on presentations\u003c\/li\u003e \u003cli\u003eStep-by-step predictive modeling\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"For Dummies","offers":[{"title":"Default Title","offer_id":47989840347365,"sku":"NP9781119267003","price":29.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119267003.jpg?v=1761785641","url":"https:\/\/k12savings.com\/products\/predictive-analytics-for-dummies-isbn-9781119267003","provider":"K12savings","version":"1.0","type":"link"}