Logistic Regression (Predictive Modeling) workshop using R

The video walks you through every stage of the model development lifecycle, completely removing the need for traditional coding:

Data Ingestion & Cleaning (01:59): Easily load various file types (CSV, Excel, etc.) and see how the tool automatically handles data cleaning and variable generation, providing transparent SQL code for every step.

Comprehensive Data Audit (03:05) : Perform full univariate and bivariate analysis, including cross-tabulation, histograms, box plots, Q-Q plots, skewness, kurtosis, and the Shapiro-Wilk test [05:20].

Variable Analysis & Binning (06:02): Visually analyze the trend of independent variables against the dependent variable (Target B). Create custom bins or use automated grouping features without writing code  (06:53).

Automated Model Building (08:02) : Select Logistic Regression and watch the platform automatically perform crucial steps like variable selection, directional/sense checks, and multicollinearity treatment to arrive at a parsimonious model.

Robust Model Validation (10:17): Review key validation metrics, including:

VIF, variable-wide coefficients, and significance.

KS Statistic and its automated interpretation 11:00.

Coefficient Stability Check and Population Stability Index (PSI) for data in and out of time (11:59).

Rich Visualization & Data Prep 12 :36 : Explore built-in dynamic visualization tools like Tree Maps and Sunburst Charts with drill-down capabilities, alongside UI-based features for joining, appending, and aggregating data .

This video is your guide to leveraging AI assistance for faster, more accurate, and more transparent credit risk modeling.