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Commercial Data Mining : Processing, Analysis and Modeling for Predictive Analytics Projects.
Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Burlington :
Elsevier Science,
2014.
|
| Colección: | Savvy manager's guides.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 049 | |a UAMI | ||
| 100 | 1 | |a Nettleton, David. | |
| 245 | 1 | 0 | |a Commercial Data Mining : |b Processing, Analysis and Modeling for Predictive Analytics Projects. |
| 260 | |a Burlington : |b Elsevier Science, |c 2014. | ||
| 300 | |a 1 online resource (361 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a The Savvy Manager's Guides | |
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Front Cover; Commercial Data Mining: Processing, Analysis and Modeling for Predictive Analytics Projects; Copyright; Contents; Acknowledgments; Chapter 1: Introduction; Chapter 2: Business Objectives; Introduction; Criteria for Choosing a Viable Project; Evaluation of Potential Commercial Data Analysis Projects -- General Considerations; Evaluation of Viability in Terms of Available Data -- Specific Considerations; Factors That Influence Project Benefits; Factors That Influence Project Costs; Example1: Customer Call Center -- Objective: IT Support for Customer Reclamations. | |
| 505 | 8 | |a Overall Evaluation of the Cost and Benefit of Mr. Strongs ProjectExample2: Online Music App -- Objective: Determine Effectiveness of Advertising for Mobile Device Apps; Overall Evaluation of the Cost and Benefit of Melody-onlines Project; Summary; Further Reading; Chapter 3: Incorporating Various Sources of Data and Information; Introduction; Data about a Businesss Products and Services; Surveys and Questionnaires; Examples of Survey and Questionnaire Forms; Surveys and Questionnaires: Data Table Population; Issues When Designing Forms; Loyalty Card/Customer Card. | |
| 505 | 8 | |a Registration Form for a Customer CardCustomer Card Registrations: Data Table Population; Transactional Analysis of Customer Card Usage; Demographic Data; The Census: Census Data, United States, 2010; Macro-Economic Data; Data about Competitors; Financial Markets Data: Stocks, Shares, Commodities, and Investments; Chapter 4: Data Representation; Introduction; Basic Data Representation; Basic Data Types; Representation, Comparison, and Processing of Variables of Different Types; Principal Types of Variables; Normalization of the Values of a Variable; Distribution of the Values of a Variable. | |
| 505 | 8 | |a Atypical Values -- OutliersAdvanced Data Representation; Hierarchical Data; Semantic Networks; Graph Data; Fuzzy Data; Chapter 5: Data Quality; Introduction; Examples of Typical Data Problems; Content Errors in the Data; Relevance and Reliability; Quantitative Evaluation of the Data Quality; Data Extraction and Data Quality -- Common Mistakes and How to Avoid Them; Data Extraction; Data Validation Filters; Derived Data; Summary of Data Extraction Example; How Data Entry and Data Creation May Affect Data Quality; Chapter 6: Selection of Variables and Factor Derivation; Introduction. | |
| 505 | 8 | |a Selection from the Available DataStatistical Techniques for Evaluating a Set of Input Variables; Correlation; Factorial Analysis; Data Fusion; Summary of the Approach of Selecting from the Available Data; Reverse Engineering: Selection by Considering the Desired Result; Statistical Techniques for Evaluating and Selecting Input Variables for a Specific Business Objective; Transforming Numerical Variables into Ordinal Categorical Variables; Customer Segmentation; Variable Selection -- Reverse Engineering; Final Segmentation Model; Summary of the Reverse Engineering Approach. | |
| 505 | 8 | |a Data Mining Approaches to Selecting Variables. | |
| 520 | |a Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling. Commercial Data. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Management |x Mathematical models. | |
| 650 | 0 | |a Management |x Data processing. | |
| 650 | 2 | |a Data Mining | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Gestion |x Modèles mathématiques. | |
| 650 | 6 | |a Gestion |x Informatique. | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Management |x Data processing |2 fast | |
| 650 | 7 | |a Management |x Mathematical models |2 fast | |
| 758 | |i has work: |a Commercial data mining (Text) |1 https://id.oclc.org/worldcat/entity/E39PCH49mVpTqhkCr6k83DgqcP |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Nettleton, David. |t Commercial Data Mining : Processing, Analysis and Modeling for Predictive Analytics Projects. |d Burlington : Elsevier Science, ©2014 |z 9780124166028 |
| 830 | 0 | |a Savvy manager's guides. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1630127 |z Texto completo |
| 938 | |a EBL - Ebook Library |b EBLB |n EBL1630127 | ||
| 994 | |a 92 |b IZTAP | ||