Data mining and predictive analytics
Larose, Daniel T.; Larose, Chantal D
Data mining and predictive analytics - 2 - New Delhi Wiley 2015 - xix, 794 p. 24 cm ; Hard
Alpha 2255/191115/Rs.9,600/-
1. An introduction to data mining and predictive analytics
2. Data pre-processing
3. Exploratory data analysis
4. Dimension-reduction methods
5. Univariate statistical analysis
6. Multivariate statistics
7. Preparing to model the data
8. Simple linear regression
9. Multiple regression and model building
10. K- nearest neighbour algorithm
11. Decision trees
12. Neural networks
13. Logistic regression
14. Naïve bayes and Bayesian networks
15. Model evaluation techniques
16. Cost- benefit analysis using data-driven costs
17. Cost benefit analysis for trainary and k-nary classification models
18. Graphical evaluation of classification models
19. Hierarchical and k-means clustering
20. Kohonen networks
21. Birch clustering
22. Measuring cluster goodness
23. Association rules
24. Segmentation models
25. Ensemable methods: bagging and boosting
26. Model voting and propensity averaging
27. Genetic algorthms
28. Imputation of missing data
29. Case study Part.1 business understanding data preparation and EDA
30. Case study Part 2 clustering and principal components analysis
31. Case study Part.3 Modelling and evaluation for performance and interpretability
32. Case study Part.4 Modelling and evaluation for high performance only.
Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis.
9781118116297
Prediction theory
Business--Data processing
Data mining
658.4038 LAR
Data mining and predictive analytics - 2 - New Delhi Wiley 2015 - xix, 794 p. 24 cm ; Hard
Alpha 2255/191115/Rs.9,600/-
1. An introduction to data mining and predictive analytics
2. Data pre-processing
3. Exploratory data analysis
4. Dimension-reduction methods
5. Univariate statistical analysis
6. Multivariate statistics
7. Preparing to model the data
8. Simple linear regression
9. Multiple regression and model building
10. K- nearest neighbour algorithm
11. Decision trees
12. Neural networks
13. Logistic regression
14. Naïve bayes and Bayesian networks
15. Model evaluation techniques
16. Cost- benefit analysis using data-driven costs
17. Cost benefit analysis for trainary and k-nary classification models
18. Graphical evaluation of classification models
19. Hierarchical and k-means clustering
20. Kohonen networks
21. Birch clustering
22. Measuring cluster goodness
23. Association rules
24. Segmentation models
25. Ensemable methods: bagging and boosting
26. Model voting and propensity averaging
27. Genetic algorthms
28. Imputation of missing data
29. Case study Part.1 business understanding data preparation and EDA
30. Case study Part 2 clustering and principal components analysis
31. Case study Part.3 Modelling and evaluation for performance and interpretability
32. Case study Part.4 Modelling and evaluation for high performance only.
Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis.
9781118116297
Prediction theory
Business--Data processing
Data mining
658.4038 LAR