sell Achten Sie aber nicht nur auf die Menge sondern auch auf die Verteilung sowie das Besucherverhalten so dass aus einem Massenprodukt ein Sondermodell wird Mass Customization Digitale Produkte Es lassen sich neue Produkte einstellen oder Rabattaktionen gestalten etc. was für Sie als Onlinehändler mehr Umsatz bedeutet SEM die Interessenten in Suchmaschinen eingeben 413 411Index Conclusions and 409Summary 407Deployment Understanding Business 405Revisit 403Modeling Preparation 403Data Data the Defining Understanding: 402Data Study Case Desk 401Help Conclusions and 401Summary 392Deployment Models 391Revisit Analysis "What-If" 385Deployment: 381Modeling Preparation 380Data Understanding 378Data Problem the Defining Understanding: 377Business Overview Study: Case Analysis 377Survey Studies Case 13 375Chapter 355Summary Steps 354Deployment Considerations Deployment 353General Deployment Model 12 352Chapter 351Summary Mining Text in Expressions Regular of 349Uses Expressions 347Regular Features Mining Text with 347Modeling Terms 347Grouping Features Keyword 346Reducing N-Grams Features: 346Multi-Word Similarity 344Cosine 344TF-IDF Frequency Document 341Inverse Frequency 340Term Features Mining 339Text Set Data Movie Polarity Sentiment 338The 337Dictionaries 337Stemming Filters Number and Length 336Character Filters Punctuation and Word 336Stop 333Tokens Tagging 333POS Steps Preparation 333Data Mining Text for Sources 332Data Applications Mining 330Text Hard Is Mining Text 329Why Data Unstructured vs. 329Structured Mining Text to Approach Modeling Predictive 328A Mining Text for 327Motivation Mining Text 11 326Chapter 323Summary Ensembles Model 323Interpreting Razor Occam's and Ensembles 321Model Ensembles 321Heterogeneous Boosting Gradient 320Stochastic Forests 320Random Boosting and Bagging to 316Improvements 311Boosting 309Bagging Tradeoff Variance 308Bias Crowds of Wisdom 307The Ensembles for 307Motivation Ensembles Model 10 304Chapter 301Summary Models Regression 293Assessing Assessment Model to Approach 284Rank-Ordered cation Classifi Correct 284Percent Assessment Model to Approach 283Batch Models Predictive Assessing 9 281Chapter 280Summary Algorithms Regression 279Other Classification for Regression Linear 278Using Models Regression Linear 276Interpreting Regression Linear in Selection 274Variable Assumptions Regression 271Linear Regression 270Linear Models 269Regression Bayes Naïve for Considerations Practical 268Other ers Classifi Bayes Naïve 268Interpreting Classifier Bayes Naïve 264The Theorem 264Bayes' Bayes 259Naïve k-NN for Considerations Practical 258Other k-NN for Metrics 254Distance Algorithm Learning k-NN 254The Neighbor 253K-Nearest Networks Neural for Considerations Practical 253Other Boundaries Decision Network 252Neural Networks Neural 251Interpreting Pruning Network 249Neural Settings Network 247Neural Networks Neural of Flexibility 244The Training Network 242Neural Neuron The Blocks: 240Building Networks 235Neural Regression Logistic for Considerations Practical 233Other Models Regression Logistic 230Interpreting Regression 229Logistic Trees Decision for Considerations Practical 224Other Costs cation Misclassifi Records: 224Reweighting Priors Records: 222Reweighting Options and Knobs Tree 221Decision Metrics Splitting Tree 218Decision Trees Decision 215Building Landscape Tree Decision 214The Trees 213Decision Modeling Predictive 8 212Chapter 210Summary Outliers 209Cluster Prototypes 203Cluster Models Cluster Forming in Variables Key 202Identifying Methods Interpretation with 199Problems Interpretation Model Cluster 199Standard Models Descriptive Interpreting 7 197Chapter 196Summary K-Means with 194Similarities Maps Kohonen 192Visualizing Algorithm SOM Kohonen 185The Clusters of Number the 183Selecting K-Means for Preparation 178Data Algorithm K-Means 177The Algorithms 174Clustering Models PCA on Magnitude Variable of Effect 172The PCA Using before Considerations 171Additional Interpretation Data for 169PCA Data New to PCA 165Applying Algorithm PCA 165The Analysis Component 164Principal Modeling Descriptive with Issues Preparation 163Data Modeling Descriptive 6 161Chapter 159Summary Rules Association from Rules Classification 159Building Rules Few 158Too Rules Many 158Too Rules 158Redundant Rules Association with 157Problems Creation Variable 157Interaction Selection 156Variable Rules Association 154Deploying Rules Interesting of 152Measures Format 151Transactional Format Data Modeling Predictive 151Standard Organized Is Data the 151How Settings 150Parameter 150Lift Accuracy dence, 149Confi Support 149Antecedent 148Support Set) (Item 148Rule Conclusion Output, Consequent, 148Right-Hand-Side, Antecedent(s) 147Left-Hand-Side, 146Condition 145Terminology Rules Association and Itemsets 5 143Chapter 139Summary Clustering K-Means for Matters Normalization Why 123Example: 117Sampling Modeling to Prior Selection 112Variable Features 110Multidimensional Best? Is Variable a of Version 110Which Features Code 109ZIP Features Variable Time and 108Date Transformations Variable 107Ordinal Transformation Variable 104Nominal Scaling Variable 103Numeric Variables Continuous 99Binning Skew 98Fixing Transformations Variable 98Simple Creation 91Feature Data Missing 90Fixing Values 89Missing Outliers 85Multidimensional 85Outliers Formats Data in 84Consistency Values 84Incorrect Cleaning 83Variable Preparation Data 4 82Chapter 81Summary Audit Data a into Together All It 80Pulling cance Signifi Statistical of Value 78The Dimensions Two Than More in 76Scatterplots Summary in Variable Target the 75Overlaying Matrices 71Scatterplot Quartet 69Anscombe's 69Scatterplots Dimensions Higher or Two Visualization, 68Data 67Crosstabs Correlations to 66Back Correlations 66Spurious 65Correlations Interactions of Explosion Combinatorial 64The Paradox Simpson's Interactions: Variable in Value 64Hidden Summaries Variable 59Multiple 58Histograms Dimension One in Visualization 55Data Assessment Variable 52Categorical Statistics 51Rank-Ordered 49Kurtosis 47Skewness Understanding Data in Statistics Simple 46Applying Distribution 45Uniform Distribution Normal 45The Deviation 45Standard 44Mean Summaries Variable 44Single Like Looks Data the 43What Understanding Data 3 42Chapter 41Summary Deployment 41Model Criteria Evaluation and Selection 41Model Objectives 40Modeling Variables Target 40The Project the for 39Data Objectives 39Business 39Overview Detection Fraud Study: 39Case Deployment 38Model Criteria Evaluation and Selection 37Model Objectives 36Modeling Variables Target 36The Competition the for 36Data Objectives 36Business 35Overview Donors Lapsed Recovering Study: 35Case Deployment Model 34Early First Models 34Building Order of Out Modeling Predictive 33Doing Criteria Success Customized 33Other Estimation for Criteria 32Success cation Classifi for Criteria 32Success Models Predictive for Success of Measures 31Defining Variable Target for Considerations 29Temporal Variable Target the 28Defining Analysis? of Unit 27Which Analysis of Unit the 26Defining Measures as Columns the 25Defining Modeling Predictive for Data 23Defining Objectives 22Business Stool Three-Legged 21The Understanding 19Business CRISP-DM Steps: Processing Analytics 19Predictive Problem the Up Setting 2 16Chapter Modeler? Predictive a Become to Needed Is Background Educational 16What Deployment in 15Obstacles Modeling with 14Obstacles Data with 14Obstacles Management in 14Obstacles Analytics Predictive Using in 13Challenges Analytics? Predictive Uses 13Who Mining Data vs. Analytics 12Predictive Contrasted Statistics and Analytics 11Predictive Analytics and 10Statistics Statistics vs. Analytics 9Predictive Analytics Predictive and Intelligence Business between 9Similarities Obvious? the State Just Models Predictive 8Do Intelligence Business vs. Analytics 6Predictive Intelligence 6Business Models Non-Parametric vs. 5Parametric Learning Unsupervised vs. 3Supervised Analytics? Predictive Is 3What Analytics? Is 1What Analytics Predictive of Overview 1 xxiChapter Introduction odass Anpassungen angezeigt werden. 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