Predictive Analytics and Data Mining

Concepts and Practice with RapidMiner

Author: Vijay Kotu,Bala Deshpande

Publisher: Morgan Kaufmann

ISBN: 0128016507

Category: Computers

Page: 446

View: 4227

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Put Predictive Analytics into Action Learn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining. You’ll be able to: 1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process. 2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases. 3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.com Demystifies data mining concepts with easy to understand language Shows how to get up and running fast with 20 commonly used powerful techniques for predictive analysis Explains the process of using open source RapidMiner tools Discusses a simple 5 step process for implementing algorithms that can be used for performing predictive analytics Includes practical use cases and examples
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Capturing, Analyzing, and Managing Word-of-Mouth in the Digital Marketplace

Author: Rathore, Sumangla

Publisher: IGI Global

ISBN: 1466694505

Category: Business & Economics

Page: 309

View: 6553

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With the growth of information technology—and the Internet in particular—many new communication channels and platforms have emerged. These platforms are focused on being not only user friendly, but also highly interactive, providing many unique ways to create and distribute content. Capturing, Analyzing, and Managing Word-of-Mouth in the Digital Marketplace explores the way these new channels and platforms affect our everyday interactions, particularly as they relate to meaning, growth, and recent trends, practices, issues, and challenges surrounding the world of modern marketing. Featuring a special emphasis on social media, blogging, viral marketing, and other forms of e-communication, this timely reference source is essential for students, researchers, academics, and marketing practitioners.
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Applying Predictive Analytics Within the Service Sector

Author: Sahu, Rajendra,Dash, Manoj,Kumar, Anil

Publisher: IGI Global

ISBN: 1522521496

Category: Business & Economics

Page: 294

View: 7940

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Value creation is a prime concern for any contemporary business. This can be accomplished through the incorporation of various techniques and processes, such as the integration of analytics to improve business functions. Applying Predictive Analytics Within the Service Sector is a pivotal reference source for the latest innovative perspectives on the incorporation of analysis techniques to enhance business performance. Examining a wide range of relevant topics, such as alternative clustering, recommender systems, and social media tools, this book is ideally designed for researchers, academics, students, professionals, and practitioners seeking scholarly material on business improvement in the service industry.
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New England Law Review: Volume 49, Number 4 - Summer 2015

Author: New England Law Review

Publisher: Quid Pro Books

ISBN: 1610278186

Category: Law

Page: 152

View: 4805

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The New England Law Review offers its issues in convenient digital formats for e-reader devices, apps, pads, and phones. This 4th issue of Volume 49 (Sum. 2015) features an extensive and important Symposium entitled "What Stays in Vegas," presented by leading scholars on the subject of privacy and big data. Contents include: "Legal Questions Raised by the Widespread Aggregation of Personal Data," by Adam Tanner "What Stays in Vegas: The Road to 'Zero Privacy,'" by David Abrams "Privacy and Predictive Analytics in E-Commerce," by Shaun B. Spencer "Privacy and Innovation: Information as Property and the Impact on Data Subjects," by Rita S. Heimes In addition, Issue 4 includes these extensive student contributions: Note, "Reforming Civil Asset Forfeiture: Ensuring Fairness and Due Process for Property Owners in Massachusetts," by Charles Basler Note, "'Mature Person Preferred': The Circuit Split on the 'Ordinary Reader' Standard for Advertisements in Violation of the Fair Housing Act," by Heather G. Reid Comment, "Ultramercial III: The Federal Circuit's Long Lesson," by Tiffany Marie Knapp Quality digital formatting includes linked notes, active table of contents, active URLs in notes, and proper Bluebook citations.
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Data Science

Concepts and Practice

Author: Vijay Kotu,Bala Deshpande

Publisher: Morgan Kaufmann

ISBN: 0128147628

Category: Computers

Page: 568

View: 6818

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Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You’ll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... Contains fully updated content on data science, including tactics on how to mine business data for information Presents simple explanations for over twenty powerful data science techniques Enables the practical use of data science algorithms without the need for programming Demonstrates processes with practical use cases Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language Describes the commonly used setup options for the open source tool RapidMiner
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Exam Prep for: Predictive Analytics and Data Mining

Author: David Mason

Publisher: Rico Publications

ISBN: N.A

Category: Education

Page: 800

View: 4232

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Computer science is the theory, experimentation, and engineering that form the basis for the design and use of computers. This book provides over 2,000 Exam Prep questions and answers to accompany the text Predictive Analytics and Data Mining Items include highly probable exam items: Poisson process, Computability, Optimality criterion, Computational humor, Denotational semantics, Fourier analysis, Concurrence, Erasing rule, Alloy, Definition, Explicit substitution, Hardness of approximation, and more.
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R: Predictive Analysis

Author: Tony Fischetti,Eric Mayor,Rui Miguel Forte

Publisher: Packt Publishing Ltd

ISBN: 1788290852

Category: Computers

Page: 1065

View: 4488

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Master the art of predictive modeling About This Book Load, wrangle, and analyze your data using the world's most powerful statistical programming language Familiarize yourself with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, Naive Bayes, decision trees, text mining and so on. We emphasize important concepts, such as the bias-variance trade-off and over-fitting, which are pervasive in predictive modeling Who This Book Is For If you work with data and want to become an expert in predictive analysis and modeling, then this Learning Path will serve you well. It is intended for budding and seasoned practitioners of predictive modeling alike. You should have basic knowledge of the use of R, although it's not necessary to put this Learning Path to great use. What You Will Learn Get to know the basics of R's syntax and major data structures Write functions, load data, and install packages Use different data sources in R and know how to interface with databases, and request and load JSON and XML Identify the challenges and apply your knowledge about data analysis in R to imperfect real-world data Predict the future with reasonably simple algorithms Understand key data visualization and predictive analytic skills using R Understand the language of models and the predictive modeling process In Detail Predictive analytics is a field that uses data to build models that predict a future outcome of interest. It can be applied to a range of business strategies and has been a key player in search advertising and recommendation engines. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. This Learning Path will provide you with all the steps you need to master the art of predictive modeling with R. We start with an introduction to data analysis with R, and then gradually you'll get your feet wet with predictive modeling. You will get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. You will be able to solve the difficulties relating to performing data analysis in practice and find solutions to working with “messy data”, large data, communicating results, and facilitating reproducibility. You will then perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. By the end of this Learning Path, you will have explored and tested the most popular modeling techniques in use on real-world data sets and mastered a diverse range of techniques in predictive analytics. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Data Analysis with R, Tony Fischetti Learning Predictive Analytics with R, Eric Mayor Mastering Predictive Analytics with R, Rui Miguel Forte Style and approach Learn data analysis using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach. This is a practical course, which analyzes compelling data about life, health, and death with the help of tutorials. It offers you a useful way of interpreting the data that's specific to this course, but that can also be applied to any other data. This course is designed to be both a guide and a reference for moving beyond the basics of predictive modeling.
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