Advances in Analytics and Applications

個数:1
紙書籍版価格
¥24,623
  • 電子書籍
  • ポイントキャンペーン

Advances in Analytics and Applications

  • 著者名:Laha, Arnab Kumar (EDT)
  • 価格 ¥18,213 (本体¥16,558)
  • Springer(2018/09/07発売)
  • 春分の日の三連休!Kinoppy 電子書籍・電子洋書 全点ポイント30倍キャンペーン(~3/22)
  • ポイント 4,950pt (実際に付与されるポイントはご注文内容確認画面でご確認下さい)
  • 言語:ENG
  • ISBN:9789811312076
  • eISBN:9789811312083

ファイル: /

Description

This book includes selected papers submitted to the ICADABAI-2017 conference, offering an overview of the new methodologies and presenting innovative applications that are of interest to both academicians and practitioners working in the area of analytics. It discusses predictive analytics applications, machine learning applications, human resource analytics, operations analytics, analytics in finance, methodology and econometric applications. The papers in the predictive analytics applications section discuss web analytics, email marketing, customer churn prediction, retail analytics and sports analytics. The section on machine learning applications then examines healthcare analytics, insurance analytics and machine analytics using different innovative machine learning techniques. Human resource analytics addresses important issues relating to talent acquisition and employability using analytics, while a paper in the section on operations analytics describe an innovative application in oil and gas industry. The papers in the analytics in finance part discuss the use of analytical tools in banking and commodity markets, and lastly the econometric applications part presents interesting banking and insurance applications.

Table of Contents

Part I:  Overviews.- Chapter 1. Machine Learning.- Chapter 2. Regression Methods.- Chapter 3. Functional Data Analysis.- Chapter 4. Directional Data Analysis.- Chapter 5.Branching Processes.- Part II: Predictive Analysis Applications.- Chapter 6. Click-Through Rate Estimation using CHAID Classification Tree Model.- Chapter 7 . Predicting Success Probability in Professional Tennis Tournaments: Using a Logistic Regression Model.- Chapter 8. Hausdorff Path Clustering and Hidden Markov Model Applied to Person Movement Prediction in Retail Spaces.- Chapter 9. Improving Email Marketing Campaign Success Rate Using Personalization.- etc.

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