LEADER 07012nam 22007335 450 001 9910300660003321 005 20260129164027.0 010 $a9781484204450 010 $a148420445X 024 7 $a10.1007/978-1-4842-0445-0 035 $a(CKB)3710000000291405 035 $a(EBL)1964920 035 $a(OCoLC)897115919 035 $a(SSID)ssj0001386707 035 $a(PQKBManifestationID)11814668 035 $a(PQKBTitleCode)TC0001386707 035 $a(PQKBWorkID)11374451 035 $a(PQKB)10545911 035 $a(MiAaPQ)EBC1964920 035 $a(DE-He213)978-1-4842-0445-0 035 $a(CaSebORM)9781484204450 035 $a(PPN)183088832 035 $a(OCoLC)899214546 035 $a(OCoLC)ocn899214546 035 $a(EXLCZ)993710000000291405 100 $a20141125d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aPredictive Analytics with Microsoft Azure Machine Learning $eBuild and Deploy Actionable Solutions in Minutes /$fby Valentine Fontama, Roger Barga, Wee Hyong Tok 205 $a1st ed. 2014. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2014. 215 $a1 online resource (178 p.) 300 $aIncludes index. 311 08$a9781484204467 311 08$a1484204468 320 $aIncludes bibliographical references and index. 327 $aContents at a Glance; Contents; About the Authors; Acknowledgments; Foreword; Introduction; Part1: Introducing Data Science and Microsoft Azure Machine Learning; Chapter 1: Introduction to Data Science; What Is Data Science?; Analytics Spectrum; Descriptive Analysis; Diagnostic Analysis; Predictive Analysis; Prescriptive Analysis; Why Does It Matter and Why Now?; Data as a Competitive Asset; Increased Customer Demand; Increased Awareness of Data Mining Technologies; Access to More Data; Faster and Cheaper Processing Power; The Data Science Process; Common Data Science Techniques 327 $aClassification AlgorithmsClustering Algorithms; Regression Algorithms; Simulation; Content Analysis; Recommendation Engines; Cutting Edge of Data Science; The Rise of Ensemble Models; Real World Applications of Ensemble Models; Building an Ensemble Model; Summary; Bibliography; Chapter 2: Introducing Microsoft Azure Machine Learning; Hello, Machine Learning Studio!; Components of an Experiment; Five Easy Steps to Creating an Experiment; Step 1: Get Data; Step 2: Preprocess Data; Step 3: Define Features; Step 4: Choose and Apply Machine Learning Algorithms; Step 5: Predict Over New Data 327 $aDeploying Your Model in ProductionDeploying Your Model into Staging; Testing the Web Service; Moving Your Model from Staging into Production; Accessing the Azure Machine Learning Web Service; Summary; Chapter 3: Integration with R; R in a Nutshell; Building and Deploying Your First R Script; Using R for Data Preprocessing; Using a Script Bundle (Zip); Building and Deploying a Decision Tree Using R; Summary; Part2: Statistical and Machine Learning Algorithms; Chapter 4: Introduction to Statistical and Machine Learning Algorithms; Regression Algorithms; Linear Regression; Neural Networks 327 $aDecision TreesBoosted Decision Trees; Classification Algorithms; Support Vector Machines; Bayes Point Machines; Clustering Algorithms; Summary; Part3: Practical Applications; Chapter 5: Building Customer Propensity Models; The Business Problem; Data Acquisition and Preparation; Loading Data from Your Local File System; Loading Data from Other Sources; Data Analysis; More Data Treatment; Feature Selection; Training the Model; Model Testing and Validation; Model Performance; Summary; Chapter 6: Building Churn Models; Churn Models in a Nutshell; Building and Deploying a Customer Churn Model 327 $aPreparing and Understanding DataData Preprocessing and Feature Selection; Classification Model for Predicting Churn; Evaluating the Performance of the Customer Churn Models; Summary; Chapter 7: Customer Segmentation Models; Customer Segmentation Models in a Nutshell; Building and Deploying Your First K-Means Clustering Model; Feature Hashing; Identifying the Right Features; Properties of K-Means Clustering; Customer Segmentation of Wholesale Customers; Loading the Data from the UCI Machine Learning Repository; Using K-Means Clustering for Wholesale Customer Segmentation 327 $aCluster Assignment for New Data 330 $aData Science and Machine Learning are in high demand, as customers are increasingly looking for ways to glean insights from all their data. More customers now realize that Business Intelligence is not enough as the volume, speed and complexity of data now defy traditional analytics tools. While Business Intelligence addresses descriptive and diagnostic analysis, Data Science unlocks new opportunities through predictive and prescriptive analysis. The purpose of this book is to provide a gentle and instructionally organized introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book also provides a thorough overview of the Microsoft Azure Machine Learning service using task oriented descriptions and concrete end-to-end examples, sufficient to ensure the reader can immediately begin using this important new service. It describes all aspects of the service from data ingress to applying machine learning and evaluating the resulting model, to deploying the resulting model as a machine learning web service. Finally, this book attempts to have minimal dependencies, so that you can fairly easily pick and choose chapters to read. When dependencies do exist, they are listed at the start and end of the chapter. The simplicity of this new service from Microsoft will help to take Data Science and Machine Learning to a much broader audience than existing products in this space. Learn how you can quickly build and deploy sophisticated predictive models as machine learning web services with the new Azure Machine Learning service from Microsoft. 606 $aArtificial intelligence 606 $aDatabase management 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 615 0$aArtificial intelligence. 615 0$aDatabase management. 615 14$aArtificial Intelligence. 615 24$aDatabase Management. 676 $a005.1 700 $aFontama$b Valentine$4aut$4http://id.loc.gov/vocabulary/relators/aut$0969213 702 $aBarga$b Roger S.$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aTok$b Wee-Hyong$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bUMI 801 1$bUMI 906 $aBOOK 912 $a9910300660003321 996 $aPredictive Analytics with Microsoft Azure Machine Learning$92202161 997 $aUNINA