Cover -- Copyright -- Credits -- Foreword -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Data Understanding -- Introduction -- Using an empty aggregate to evaluate sample size -- Evaluating the need to sample from the initial data -- Using CHAID stumps when interviewing an SME -- Using a single cluster K-means as an alternative to anomaly detection -- Using an @NULL multiple Derive to explore missing data -- Creating an outlier report to give to SMEs -- Detecting potential model instability early using the Partition node and Feature Selection -- Chapter 2: Data Preparation - Select -- Introduction -- Using the Feature Selection node creatively to remove, or decapitate, perfect predictors -- Running a Statistics node on anti-join to evaluate potential missing data -- Evaluating the use of sampling for speed -- Removing redundant variables using correlation matrices -- Selecting variable using the CHAID modeling node -- Selecting variables using the Means node -- Selecting variables using single-antecedent association rules -- Chapter 3: Data Preparation - Clean -- Introduction -- Binning scale variables to address missing data -- Using a full data model/partial data model approach to address missing data -- Imputing in-stream mean or median -- Imputing missing values randomly from uniform or normal distributions -- Using random imputation to match a variable's distribution -- Searching for similar records using a neural network for inexact matching -- Using neuro-fuzzy searching to find similar names -- Producing longer Soundex codes -- Chapter 4: Data Preparation - Construct -- Introduction -- Building transformations with multiple Derive nodes -- Calculating and comparing conversion rates -- Grouping categorical values.

Transforming high skew and kurtosis variables with a multiple Derive node -- Creating flag variables for aggregation -- Using Association Rules for interaction detection/feature creation -- Creating time-aligned cohorts -- Chapter 5: Data Preparation - Integrate and Format -- Introduction -- Speeding up merge with caching and optimization settings -- Merging a look-up table -- Shuffle-down (nonstandard aggregation) -- Cartesian product merge using key-less merge by key -- Multiplying out using Cartesian product merge, user source, and derive dummy -- Changing large numbers of variable names without scripting -- Parsing nonstandard dates -- Parsing and performing a conversion on a complex stream -- Sequence processing -- Chapter 6: Selecting and Building a Model -- Introduction -- Evaluating balancing with the Auto Classifier -- Building models with and without outliers -- Neural Network Feature Selection -- Creating a bootstrap sample -- Creating bagged logistic regression models -- Using KNN to match similar cases -- Using Auto Classifier to tune models -- Next-Best-Offer for large datasets -- Chapter 7: Modeling - Assessment, Evaluation, Deployment, and Monitoring -- Introduction -- How (and why) to validate as well as test -- Using classification trees to explore the predictions of a Neural Net -- Correcting a confusion matrix for an imbalanced target variable by incorporating priors -- Using aggregate to write cluster centers to Excel for conditional formatting -- Creating a classification tree financial summary using aggregate and an Excel Export node -- Reformatting data for reporting with a Transpose node -- Changing formatting of fields in a Table node -- Combining generated filters -- Chapter 8: CLEM Scripting -- Introduction -- Building iterative Neural Network forecasts -- Quantifying variable importance with Monte Carlo Simulation.

Implementing champion/challenger model management -- Detecting Outliers with the jackknife method -- Optimizing K-means cluster solutions -- Automating time series forecasts -- Automating HTML reports and graphs -- Rolling your own modeling algorithm: Weibull analysis -- Appendix: Business Understanding -- Introduction -- Define business objectives by Tom Khabaza -- Assessing the situation by Meta Brown -- Translating your business objective into a data mining objective by Dean Abbott -- Produce a project plan - ensuring a realistic timeline by Keith McCormick -- Index.