000			02085nam a22001937a 4500
020			_a9781118116297
082			_a658.4038 LAR
100			_aLarose, Daniel T.; Larose, Chantal D
245			_aData mining and predictive analytics
250			_a2
260			_aNew Delhi _bWiley _c2015
300			_axix, 794 p. _b24 cm ; Hard
500			_aAlpha 2255/191115/Rs.9,600/-
505			_a1. 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.
520			_aLearn 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.
650			_aPrediction theory _aBusiness--Data processing _aData mining
856			_uhttp://as.wiley.com/WileyCDA/WileyTitle/productCd-1118116194.html
942			_cBK
999			_c102716 _d102716