Logistic Regression (Credit Scoring) Modeling using SAS [FIRSTTHINKER]seeders: 2
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Logistic Regression (Credit Scoring) Modeling using SAS [FIRSTTHINKER] (Size: 1.14 GB)
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Logistic Regression (Credit Scoring) Modeling using SAS
Analytics / Machine Learning / Data Science: Statistical / Econometrics foundation, SAS Program details, Modeling demo Instructed by Gopal Prasad Malakar Business / Data & Analytics Course Description What is this course all about? This course is all about credit scoring / logistic regression model building using SAS. It explains There course promises to explain concepts in a crystal clear manner. It goes through the practical issue faced by analyst. Some of the discussion item would be How to clarify objective and ensure data sufficiency? How do you decide the performance window? How do you perform data treatment How to go for variable selection? How to deal with numeric variables and character variables? How do you treat multi collinerity scientifically? How do you understand the strength of your model? How do you validate your model? How do you interpret SAS output and develop next SAS code accordingly? Step by step workout - model development on an example data set What kind of material is included? It consists of video recording of screen (audio visual screen capture), pdf of presentations, Excel data for workout, word document containing code and Excel document containing step by step model development workout details How long the course will take to complete? Approximately 30 hours How is the course structured? It has seven sections, which step by step explains model development Why Take this course? The course is more intended towards students / analytics professionals to Get crystal clear understanding Get jobs in this kind of work by clearing interview with confidence Be successful at their statistical or analytical profession due to the quality output they produce What are the requirements? Basic knowledge of SAS What am I going to get from this course? Over 85 lectures and 16 hours of content! Learn model development Understand the science behind model development Understand the SAS program required for various steps Get comfortable with interpretation of SAS program output See the step by step model development What is the target audience? Students Analysts / Analytics professional Modelers / Statisticians Curriculum Section 1: Course Outline Lecture 1 Course content Preview 06:31 Lecture 2 Introduction to logistic Regression Modelling - High level Preview 08:34 Lecture 3 Udemy Content details - Model workout details and excel file downloads Preview 03:32 Lecture 4 Tips for Students 02:55 Lecture 5 Course Content PDF 3 pages Section 2: Introduction to Credit Scoring / Credit Score card development Lecture 6 Section outline Preview 01:23 Lecture 7 3C Concept of Credit Approval Process 15:31 Lecture 8 High Level Understanding of Score 09:31 Lecture 9 Benefit of scoring (modelling) 20:25 Lecture 10 Introduction to modeling 07:09 Lecture 11 Types of scores Preview 12:47 Lecture 12 A typical risk score 04:16 Lecture 13 Section PDF 20 pages Section 3: Data Design for Modelling Lecture 14 Section outline Preview 02:44 Lecture 15 Model Design Example 17:54 Lecture 16 Model Design - definitions and pointers 13:19 Lecture 17 Decide Performance window by Vintage Analysis Preview 14:51 Lecture 18 Model Design Precaution 08:18 Lecture 19 Section PDF 20 pages Section 4: Data Audit - Make sure to check that data is right for the modelling Lecture 20 Section Outline Preview 03:59 Lecture 21 Essential Data Quality 03:45 Lecture 22 How to download excel / word files ? Preview 02:39 Lecture 23 Feel the data - know it's contents 09:02 Lecture 24 Feel the data - View it's contents 09:29 Lecture 25 Feel the data - know it's distinct values 09:02 Lecture 26 Feel the data - know it's distribution 13:27 Lecture 27 Feel the data - Understand Coefficient of variance (need and applicability) 08:16 Lecture 28 Feel the data - know kurtosis and skewness 04:47 Lecture 29 Feel the data - know the percentile 11:21 Lecture 30 Feel the data - know stem n leaf diagram Preview 05:35 Lecture 31 Feel the data - Understand box plot to detect outliers 06:15 Lecture 32 Feel the data - Understand and interpret normal probability plot 22:27 Lecture 33 Missing Value treatment And Flooring / Capping Guidiline 13:49 Lecture 34 Section PDF 31 pages Section 5: Variable Selection - Select important numeric and character variables Lecture 35 Section Outline Preview 03:00 Lecture 36 Variable Selection - High level and flow chart of steps 13:04 Lecture 37 Important Character / Categorical Variable selection - high level Preview 06:07 Lecture 38 Understand Chi-Square statistics for selecting Important Categorical Variables 19:52 Lecture 39 Getting Chi-Square statistics using SAS 08:36 Lecture 40 Data Workout - Preamble 11:02 Lecture 41 Model Workout - 01 Data Treatment 34:52 Lecture 42 Numeric Variable Selection - Part 01 10:43 Lecture 43 SAS Macro to check directional sense of numeric variable 14:58 Lecture 44 Recap Linear Regression 04:08 Lecture 45 Introduction to Logistic Regression 11:57 Lecture 46 Theory and Example of Step wise selection of Numeric Variable 19:53 Lecture 47 Appendix - Fisher's linear discriminant function to select important numeric Var 09:23 Lecture 48 Appendix - Information Value method of selecting important variables (all types) 10:07 Lecture 49 Appendix -Phi Square and Cramer's V for important categorical variable selection 06:59 Lecture 50 Section PDF 64 pages Section 6: Multi Collinearity Treatment Lecture 51 Section Outline Preview 03:12 Lecture 52 Common Sense Understanding of Multi collinearity and it's impact 07:02 Lecture 53 Detecting Multi Collinearity 10:10 Lecture 54 Multi Collinearity Treatment - part 01 19:20 Lecture 55 Multi Collinearity Treatment - part 02 05:58 Lecture 56 Model Data workout - 02 Bi Variate strength of variables 09:41 Lecture 57 Model Data workout - 03 Multi Collinearity Treatment (Scientifically) 12:36 Lecture 58 Section PDF 24 pages Section 7: Iterate for final model / Understand strength of the model Lecture 59 Section Outline Preview 03:05 Lecture 60 Introduction to final model development steps 04:07 Lecture 61 Logistic Model Information - part 01 05:53 Lecture 62 Logistic Model Information - part 02 04:48 Lecture 63 Model Fit Statistics 02:58 Lecture 64 Log Likelihood 15:12 Lecture 65 Log Likelihood ratio - part 01 06:28 Lecture 66 Log Likelihood Ratio - part 02 03:29 Lecture 67 Model Fit Statistics - Revisit 13:23 Lecture 68 Maximum Likelihood Estimate 12:44 Lecture 69 Concordance, Somer's D, Gamma, Tau etc. Preview 18:47 Lecture 70 Ideal logistic regression output 04:17 Lecture 71 Model Data Workout - part 04 Try Model on 10 variables 06:50 Lecture 72 Model Data Workout - part 05 Select best 8 variables 09:26 Lecture 73 Section PDF 39 pages Section 8: Strength of a Model and Model Validation Methods Lecture 74 Section Outline Preview 02:21 Lecture 75 Model Data Workout - part 06 Coefficient Stability Check 11:28 Lecture 76 Understand Score and Generate Score in the data set 12:21 Lecture 77 Theoretical Understanding of KS Preview 03:59 Lecture 78 Model Data Workout - part 08 Generate KS Statistics for the model 20:51 Lecture 79 Model Data Workout - part 09 Understand and Generate Gini Statistics 11:30 Lecture 80 Model Data Workout - part 10 Understand & Apply Model Validation n Stability Chk 07:56 Lecture 81 Model Presentation Guideline - What should be presented to business 05:00 Lecture 82 Final Words 01:59 Lecture 83 Section PDF 25 pages Lecture 84 How to download excel / word files ? 2 pages Lecture 85 FAQ by students of this course (will keep growing overtime) Text Instructor Biography Gopal Prasad Malakar , Credit Card Analytics Professional - Trains on Data Mining I am a seasoned Analytics professional with 14+ years of professional experience. I have industry experience of impactful and actionable analytics. I am a keen trainer, who believes that training is all about making users understand the concepts. If students remain confused after the training, the training is useless. I ensure that after my training, students (or partcipants) are crystal clear on how to use the learning in their business scenarios. My expertise is in Credit Card Business, Scoring (econometrics based model development), score management, loss forecasting and MS access based database application development. Sharing Widget |