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Informationen zum Autor 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. Klappentext Real-world tips for creating business value Details on modeling, data clustering, and more Enterprise use cases to help you get started Learn to predict the future! Business 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. Inside... How to start a project Identifying data types Modeling tips Working with algorithms How data clustering works How data classification works How deep learning works Advice on presentations Step-by-step predictive modeling Zusammenfassung 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. Inhaltsverzeichnis 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 Explori...
