Tuesday, February 3, 2009

Prediction Model : Step-wise Guide

Data Preparation
o   Business goal.
o   Data sources and merges.
o   Data segmentation-based on product type, other characteristic (utilization or sales) or based on data availability.
o   In sample and out sample data.
o   In case no out sample, creation of Training and Testing datasets from the overall data.

Data Study
o   Contents, Frequency and Univariate procedures.
o   Understanding data distribution (Mean, Median, Mode, Min, Max and Missing).

Data Cleaning
o   Missing Imputation
o   Capping and Flooring (99% or 95% truncations and lower end truncations at 1% (usually)‏)

Derived Variable Creation
o   Representing Character Variables in Numeric Format
o   Flags for Missing etc.
o   Interaction variables using CART/CHAID
o   Transformation (Exponential/Logarithmic)

Variable Reduction
o   Chi-square test & T-test.
o   Cluster Analysis.
o   Factor Analysis.
o   Information Value

Data Segmentation.
o   Model segments based on CART or other characteristics (like-delinquent/non-delinquent).

Rank plots and Transformations.
o   Dummy variable, log, square, square root, exponential, etc. transformations.

Models
o   Linear/Logit /Multinomial Logit etc.
o   Step wise model (backward, forward).

Diagnostic Check.
o   Correlation justifies the Business Sense for all Model variables.
o   All Model variables have same sign for Correlation with DV and Model Coefficient
o   Multicollinearity (Condition Index / VIF)

Model Checks.
o   Correct signs and significance.
o   Actual Vs Predicted (in-sample and out-sample).
o   Lorenz curves (in-sample and out-sample).

Model Comparisons.
o   With Existing model scores
o   Using Gains Table, Gains Chart, Market Richness Chart etc.

 Model Stability Checks
o   Business Checks
o   Statistical Test running CART on Residual DV

Timely Review
o   Review the model and the business environment at times (at least twice a year).  

1 comment:

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