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).