000 nam a22 4500
999 _c114375
_d114375
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008 190130b ||||| |||| 00| 0 eng d
020 _a9781118879368
040 _c
082 _a658.40380
_bSHM
100 _aShmueli, Galit
_934955
245 _aData Mining for Business Analytics: Concepts, Techniques and Applications in R
_cby Galit Shmueli, Peter C. Bruce, Inbal Yahav, Nitin R. Patel and Kenneth C. Lichtendahl
260 _bJohn Wiley & Sons, Inc.
_c2018
_aHoboken
300 _a544p
500 _aPart I: Preliminaries 1. Introduction 2. Overview of the data mining process Part II: Data Exploration and Dimension Reduction 3. Data visualization 4. Dimension reduction Part III: Performance Evaluation 5. Evaluating predictive performance Part IV: Prediction and Classification Methods 6. Multiple linear regression 7. k-Nearest Neighbors (kNN) 8. The Naive Bayes classifier 9. Classification and regression trees 10. Logistic regression 11. Neural nets 12. Discriminant analysis 13. Combining methods : ensembles and uplift modeling Part V: Mining Relationships Among Records 14. Association rules and collaborative filtering 15. Cluster analysis Part VI: Forecasting Time Series 16. Handling time series 17. Regression-based forecasting 18. Smoothing methods Part VII: Data Analytics 19. Social network analytics 20. Text mining Part VIII: Cases 21. Cases
600 _aBusiness mathematics - Computer programs
_937239
700 _aBruce, Peter C.
_937240
700 _aYahav, Inbal
_937241
700 _aPatel, Nitin R.
_937242
700 _aLichtendahl, Kenneth C.
_937243
942 _2ddc
_cLB
_k658.40380
_mSHM