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    Logistic Regression using SAS - Indepth Predictive Modeling

    BY
    Udemy

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    Mode

    Online

    Fees

    ₹ 3099

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 3,099
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Course Outline

    • Course content
    • Introduction to logistic Regression Modelling - High level
    • Udemy Content details - Model workout details and excel file downloads
    • Tips for Students
    • Course Content PDF

    Introduction to Credit Scoring / Credit Score card development

    • Section outline
    • 3C Concept of Credit Approval Process
    • High Level Understanding of Score
    • Benefit of scoring (modelling)
    • Introduction to modeling
    • Types of scores
    • A typical risk score
    • Introduction to Scoring FAQ
    • Section PDF

    Data Design for Modelling

    • Section outline
    • Model Design Example
    • Model Design - definitions and pointers
    • Decide Performance window by Vintage Analysis
    • Model Design Precaution
    • FAQ : for model design section
    • Section PDF

    Data Audit - Make sure to check that data is right for the modelling

    • Section Outline
    • Essential Data Quality
    • Getting free access to SAS
    • If by chance: you are uncomfortable with SAS?
    • How to download excel / SAS code / word document etc.
    • Feel the data - know it's contents
    • Feel the data - View it's contents
    • Feel the data - know it's distinct values
    • Feel the data - know it's distribution
    • Feel the data - Understand Coefficient of variance (need and applicability)
    • Feel the data - know kurtosis and skewness
    • Feel the data - know the percentile
    • Feel the data - know stem n leaf diagram
    • Feel the data - Understand box plot to detect outliers
    • Feel the data - Understand and interpret normal probability plot
    • Missing Value treatment And Flooring / Capping Guidiline
    • Section FAQ- for variable treatment
    • Check basic understanding of model design
    • Check basic understanding of data audit
    • Section PDF

    Variable Selection - Select important numeric and character variables

    • Section Outline
    • Variable Selection - High level and flow chart of steps
    • Important Character / Categorical Variable selection - high level
    • Understand Chi-Square statistics for selecting Important Categorical Variables
    • Getting Chi-Square statistics using SAS
    • Data Workout - Preamble
    • Model Workout - 01 Data Treatment
    • Numeric Variable Selection - Part 01
    • SAS Macro to check directional sense of numeric variable
    • Dealing with Independent date variables (date variables as Xs)
    • Recap Linear Regression
    • Introduction to Logistic Regression
    • Theory and Example of Step wise selection of Numeric Variable
    • Appendix - Fisher's linear discriminant function to select important numeric Var
    • Appendix - Information Value method of selecting important variables (all types)
    • Appendix -Phi Square and Cramer's V for important categorical variable selection
    • Section FAQ - for variable selection
    • Section PDF

    Multi Collinearity Treatment

    • Section Outline
    • Common Sense Understanding of Multi collinearity and it's impact
    • Detecting Multi Collinearity
    • Multi Collinearity Treatment - part 01
    • Multi Collinearity Treatment - part 02
    • Model Data workout - 02 Bi Variate strength of variables
    • Model Data workout - 03 Multi Collinearity Treatment (Scientifically)
    • FAQ for multi collinearity section
    • Section PDF

    Iterate for final model / Understand strength of the model

    • Section Outline
    • Introduction to final model development steps
    • Logistic Model Information - part 01
    • Logistic Model Information - part 02
    • Model Fit Statistics
    • Log Likelihood
    • Log Likelihood ratio - part 01
    • Log Likelihood Ratio - part 02
    • Model Fit Statistics - Revisit
    • Maximum Likelihood Estimate
    • Concordance, Somer's D, Gamma, Tau etc.
    • Ideal logistic regression output
    • Model Data Workout - part 04 Try Model on 10 variables
    • Model Data Workout - part 05 Select best 8 variables
    • Section PDF

    Strength of a Model and Model Validation Methods

    • Section Outline
    • Model Data Workout - part 06 Coefficient Stability Check
    • Understand Score and Generate Score in the data set
    • Theoretical Understanding of KS
    • Model Data Workout - part 08 Generate KS Statistics for the model
    • Model Data Workout - part 09 Understand and Generate Gini Statistics
    • Model Data Workout - part 10 Understand & Apply Model Validation n Stability Chk
    • FAQ - for strength of the model section
    • Model Presentation Guideline - What should be presented to business
    • Section PDF

    Reject Inference - Developing application score on scored population

    • Section Overview
    • Introduction to reject inference! What it is? Why it is needed?
    • How to do reject inference?
    • Impact of the new model - swapset analysis / more base with same approval rate
    • Swapset analysis supplementary video
    • Do you need reject inference all the time?

    Appendix Topics (It will have contents based on student's demands)

    • Cross Validation Techniques (Holdout, K-Fold, Out of time, all but on etc.)
    • K fold validation using simple SAS macro
    • FAQ by students of this course (will keep growing overtime)
    • Introduction to Multinomial Logistic Regression and solution approach
    • Demo of multinomial logistic regression using SAS
    • Ordinal Logistic Regression and Proportional Odds assumption
    • About Data used for Ordinal Logistic Regression Demo
    • Demo of Ordinal Logistic Regression using SAS
    • Count Data Model - Poisson Regression
    • Bonus Topic - how to learn Predictive Modeling / Logistic Regression with R
    • Bonus Topic - Analytics / Data Science / Machine Learning Interview questions
    • Final Words

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