Predictive Analytics and Data Mining

Author: Vijay Kotu
Publisher: Morgan Kaufmann
ISBN: 9780128016503
Release Date: 2014-11-27
Genre: Computers

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

Predictive Analytics

Author: Eric Siegel
Publisher: John Wiley & Sons
ISBN: 9781119145684
Release Date: 2016-01-13
Genre: Business & Economics

"Mesmerizing & fascinating..." —The Seattle Post-Intelligencer "The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive Analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated — and Hillary for America 2016 plans to calculate — the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

Applying Predictive Analytics Within the Service Sector

Author: Sahu, Rajendra
Publisher: IGI Global
ISBN: 9781522521495
Release Date: 2017-02-07
Genre: Business & Economics

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.

Capturing Analyzing and Managing Word of Mouth in the Digital Marketplace

Author: Rathore, Sumangla
Publisher: IGI Global
ISBN: 9781466694507
Release Date: 2015-08-28
Genre: Business & Economics

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.

Business Intelligence

Author: Roland M. Müller
Publisher: Springer-Verlag
ISBN: 9783642355608
Release Date: 2013-11-19
Genre: Computers

Das Buch befasst sich mit der Bereitstellung von Daten und Verfahren für analytische Zwecke (Planung, Entscheidung, Controlling sowie Fehlerrückverfolgung) in Unternehmen sowie der notwendigen Rechenleistungen. Die Autoren erläutern die Datenbereitstellung mittels Data Warehouses, Auswertung mittels OLAP-Operationalität und geeignete Verfahren der explorativen Datenanalyse. Bei den Verfahren des Operations Research werden Simulation und Lineare Optimierung dargestellt. Neben erfolgreichen Anwendungen und Fallstudien steht das Verständnis der zugrundeliegenden Algorithmen und Datenstrukturen, die für das Erlernen der BI-Verfahren zwingend notwendig sind, im Vordergrund.

Computer

Author: Rolf Drechsler
Publisher: Springer-Verlag
ISBN: 9783662530603
Release Date: 2017-02-23
Genre: Technology & Engineering

Computer umgeben uns heute in fast allen Lebensbereichen. Sie erleichtern uns nicht nur als PC oder Laptop die Arbeit, sondern sind auch eingebettet in zahlreiche Objekte unseres täglichen Lebens – vom Auto bis zur Waschmaschine. Doch wie funktionieren moderne Rechner eigentlich? Und wie werden diese hochkomplexen, aus Milliarden Komponenten bestehenden Geräte entworfen? Das Buch erklärt auf verständliche, informative und unterhaltsame Weise den Aufbau und die Funktionsweise heutiger Computersysteme. Einzelne inhaltliche Abschnitte werden durch Links zu Videos ergänzt, in denen Professor Rolf Drechsler relevante Themen prägnant und pointiert vorstellt.

Informatik kompakt

Author: Katharina Morik
Publisher: Springer-Verlag
ISBN: 9783540292753
Release Date: 2005-12-20
Genre: Computers

Die Autoren geben eine fundierte Einführung in die Informatik, die von Anfang an die Zusammenhänge zwischen den Teilgebieten des Faches betont. Das Buch ist kompakt, weil der gemeinsame Kern der verschiedenen Informatikgebiete betrachtet wird. In einer integrativen Sichtweise werden Modellierung, abstrakte Datentypen, Algorithmen sowie nebenläufige und verteilte Programmierung behandelt. Die grundlegenden Konzepte der Informatik werden dabei mittels der Programmiersprache Java realisiert. Wesentliches Anliegen der Autoren ist es, die Informatik als Wissenschaft der Abstraktion herauszustellen und in diesem Sinne den Studierenden allgemeine Methoden zum Lösen praktischer Probleme zu vermitteln. Lernkontrollen und ein effektiver Index, der vor allem diejenigen Begriffe aufführt, die ein Informatiker einfach können muss, ermöglichen ein fokussiertes Studium. Ferner stehen vielfältige Programm-Beispiele im Internet bereit.

Data Mining

Author: Gholamreza Nakhaeizadeh
Publisher: Physica
ISBN: 3790810533
Release Date: 1998-01-15
Genre: Business & Economics

Das Buch befaßt sich mit theoretischen und Anwendungsaspekten des Data Mining und behandelt unter anderem folgende Themen: Ziele und Methoden des Data Mining, Prozeß der Wissensentdeckung, State of the Art in der Forschung und Anwendung des Data Mining, wichtige Data Mining Tools, die Rolle der Informationsverarbeitung im KDD Prozeß, Data Warehousing, OLAP, Ansätze zur Benutzerunterstützung des Data Mining Prozesses, Modellselektion und Evaluierungsmethoden für Data Mining Algorithmen.

Datenflut und Informationskan le

Author: Heike Ortner
Publisher: innsbruck University Press
ISBN: 9783903122208
Release Date: 2016-09-29
Genre: Business & Economics

Im Digitalzeitalter haben die Produktion, Verbreitung und Speicherung von Daten gigantische Ausmaße angenommen. Pro Minute werden weltweit fast 140 Millionen E-Mails verschickt, 100 Stunden Videomaterial auf YouTube hochgeladen, 350.000 Tweets geschrieben, 970 neue Blogeinträge von Wordpress-Usern veröffentlicht und 240.000 Fotos auf Facebook hochgeladen — Tendenz steigend. Abgesehen von der expliziten Erstellung von Daten sind wir alle selbst als Mediennutzer und Konsumenten Datenquellen. Diese Daten sind bereits zu einem monetär relevanten, maßgeblichen Bestandteil gezielten Marketings geworden. Unter dem Schlagwort „Open Data“ wird auch gegenüber dem Staat gefordert, öffentliche Verwaltungsdaten für alle verfügbar und nutzbar zu machen. Gleichzeitig bieten Enthüllungsplattformen à la WikiLeaks gerade geheimen und vertraulichen Daten eine breite Öffentlichkeit. Und auch immer mehr Unternehmen und politische Parteien wollen aus der Datenflut im Netz Profit schlagen. Mit statistisch-algorithmischen Methoden wird beim sogenannten „data mining“ versucht, Wissenswertes aus dem Datenberg ans Licht zu befördern. „Digital Humanities“ verfolgen das Ziel, neue Fragestellungen und Erkenntnismodelle für die Geisteswissenschaften zu generieren.

R Predictive Analysis

Author: Tony Fischetti
Publisher: Packt Publishing Ltd
ISBN: 9781788290852
Release Date: 2017-03-31
Genre: Computers

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.

Business Intelligence

Author: CTI Reviews
Publisher: Cram101 Textbook Reviews
ISBN: 9781467234238
Release Date: 2016-09-26
Genre: Education

Facts101 is your complete guide to Business Intelligence. In this book, you will learn topics such as Business Performance Management, Data Mining for Business Intelligence, Text and Web Mining, and Business Intelligence Implementation: Integration and Emerging Trends plus much more. With key features such as key terms, people and places, Facts101 gives you all the information you need to prepare for your next exam. Our practice tests are specific to the textbook and we have designed tools to make the most of your limited study time.