(eBook PDF) Handbook of Statistical Analysis and Data Mining Applications 2nd Edition – Digital Ebook – Instant Delivery Download

Product details:

• ISBN-10 ‏ : ‎ 0124166326
• ISBN-13 ‏ : ‎ 978-0124166325
• Author: Ken Yale (Author), Robert Nisbet (Author), Gary D. Miner (Author)

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application.

This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas―from science and engineering, to medicine, academia and commerce.

Table contents:

Part I: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process

Chapter 1: The Background for Data Mining Practice

Chapter 2: Theoretical Considerations for Data Mining

Chapter 3: The Data Mining and Predictive Analytic Process

Chapter 4: Data Understanding and Preparation

Chapter 5: Feature Selection

Chapter 6: Accessory Tools for Doing Data Mining

Part II: The Algorithms and Methods in Data Mining and Predictive Analyti

Chapter 7: Basic Algorithms for Data Mining: A Brief Overview

Chapter 8: Advanced Algorithms for Data Mining

Chapter 9: Classification

Chapter 10: Numerical Prediction

Chapter 11: Model Evaluation and Enhancement

Chapter 12: Predictive Analytics for Population Health and Care

Chapter 13: Big Data in Education: New Efficiencies for Recruitment,

Chapter 14: Customer Response Modeling

Chapter 15: Fraud Detection

Part III: Tutorials and Case Studies

Chapter 16: The Apparent Paradox of Complexity in Ensemble Model

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