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Data mining and predictive analytics Daniel T. Larose, Chantal D. Larose

By: Contributor(s): Material type: TextTextPublication details: New Delhi : Wiley, 2018Edition: 2nd edDescription: xxix, 794 p. : ill. ; 25 cmISBN:
  • 9788126559138
Subject(s): DDC classification:
  • 005.74  LAR
Summary: Part I. Data Preparation Chapter 1. An Introduction to Data Mining and Predictive Analytics Chapter 2. Data Preprocessing Chapter 3. Exploratory Data Analysis Chapter 4. Dimension-Reduction Methods Part II. Statistical Analysis Chapter 5. Univariate Statistical Analysis Chapter 6. Multivariate Statistics Chapter 7. Preparing to Model the data Chapter 8. Simple Linear Regression Chapter 9. Multiple Regression and Model Building Part III. Classification Chapter 10. k-Nearest Neighbor Algorithm Chapter 11. Decision trees Chapter 12. Neural Networks Chapter 13. Logistic Regression Chapter 14. Naïve Bayes and Bayesian Networks Chapter 15. Model Evaluation Techniques Chapter 16. Cost-Benefit Analysis Using Data-Driven Costs Chapter 17. Cost-Benefit Analysis For Trinary and k-Nary Classification Models Chapter 18. Graphical Evaluation of Classification Models Part IV. Clustering Chapter 19. Hierarchical and k-Means Clustering Chapter 20. Kohonen Networks Chapter 21. Birch Clustering Chapter 22. Measuring Cluster Goodness Part V. Association Rules Chapter 23. Association Rules Part VI. Enhancing Model Performance Chapter 24. Segmentation Models Chapter 25. Ensemble Methods: Bagging and Boosting Chapter 26. Model Voting and Propensity Averaging Part VII. Further Topics Chapter 27. Genetic Algorithms Chapter 28. Imputation of Missing Data Part VIII. Case Study: Predicting Response to Direct-Mail Marketing Chapter 29. Case Study, Part 1: Business Understanding, Data Preparation, and Eda Chapter 30. Case Study, Part 2: Clustering and Principal Components Analysis Chapter 31. Case Study, Part 3: Modeling And Evaluation For Performance And Interpretability Chapter 32. Case Study, Part 4: Modeling And Evaluation For High Performance Only
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Item type Current library Collection Call number Status Date due Barcode
Books Books Symbiosis Centre for Information Technology ISSUABLE  Text Book 006.312 LAR (Browse shelf(Opens below)) Available SCIT-B-10146
Books Books Symbiosis Centre for Management and Human Resource Development Computer Networks Text Book 006.312 (Browse shelf(Opens below)) Available SCMHRD-B-27996
Books Books Symbiosis Centre for Management and Human Resource Development Computer Networks Text Book 006.312 (Browse shelf(Opens below)) Available SCMHRD-B-27997
Books Books Symbiosis School for Liberal Arts 005.74 / LAR (Browse shelf(Opens below)) Available SSLA-B-8206
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006.312 KAN Data Mining : 006.312 KAN Data Mining : 006.312 KAR Learning Spark ; 006.312 LAR Data mining and predictive analytics 006.312 MAH Data Analytics 006.312 MAH Data Analytics 006.312 MUN Automated Data Collection With R :

Part I. Data Preparation
Chapter 1. An Introduction to Data Mining and Predictive Analytics
Chapter 2. Data Preprocessing
Chapter 3. Exploratory Data Analysis
Chapter 4. Dimension-Reduction Methods
Part II. Statistical Analysis
Chapter 5. Univariate Statistical Analysis
Chapter 6. Multivariate Statistics
Chapter 7. Preparing to Model the data
Chapter 8. Simple Linear Regression
Chapter 9. Multiple Regression and Model Building
Part III. Classification
Chapter 10. k-Nearest Neighbor Algorithm
Chapter 11. Decision trees
Chapter 12. Neural Networks
Chapter 13. Logistic Regression
Chapter 14. Naïve Bayes and Bayesian Networks
Chapter 15. Model Evaluation Techniques
Chapter 16. Cost-Benefit Analysis Using Data-Driven Costs
Chapter 17. Cost-Benefit Analysis For Trinary and k-Nary Classification Models
Chapter 18. Graphical Evaluation of Classification Models
Part IV. Clustering
Chapter 19. Hierarchical and k-Means Clustering
Chapter 20. Kohonen Networks
Chapter 21. Birch Clustering
Chapter 22. Measuring Cluster Goodness
Part V. Association Rules
Chapter 23. Association Rules
Part VI. Enhancing Model Performance
Chapter 24. Segmentation Models
Chapter 25. Ensemble Methods: Bagging and Boosting
Chapter 26. Model Voting and Propensity Averaging
Part VII. Further Topics
Chapter 27. Genetic Algorithms
Chapter 28. Imputation of Missing Data
Part VIII. Case Study: Predicting Response to Direct-Mail Marketing
Chapter 29. Case Study, Part 1: Business Understanding, Data Preparation, and Eda
Chapter 30. Case Study, Part 2: Clustering and Principal Components Analysis
Chapter 31. Case Study, Part 3: Modeling And Evaluation For Performance And Interpretability
Chapter 32. Case Study, Part 4: Modeling And Evaluation For High Performance Only

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