Predictive Modeling with Logistic Regression using SAS

Ce cours n'est pas disponible en Français (France)

Nous sommes actuellement en train de le traduire dans plus de langues. Consultez les langues disponibles.

Predictive Modeling with Logistic Regression using SAS

Ce cours fait partie de SAS Statistical Business Analyst Certificat Professionnel

Enseigné en Français (doublage IA)

Instructeur : Marc Huber

8 237 déjà inscrits

Inclus avec

7 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

63 avis

niveau Intermédiaire

Certaines connaissances prérequises

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

7 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

63 avis

niveau Intermédiaire

Certaines connaissances prérequises

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Compétences que vous acquerrez

Catégorie : Logistic Regression
Catégorie : Statistical Machine Learning
Catégorie : Big Data
Catégorie : Classification And Regression Tree (CART)
Catégorie : Feature Engineering
Catégorie : Statistical Analysis
Catégorie : Data Preprocessing
Catégorie : Model Evaluation
Catégorie : Regression Analysis
Catégorie : Predictive Modeling

Outils que vous découvrirez

Catégorie : SAS (Software)

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Évaluations

40 devoirs

Enseigné en Français (doublage IA)

Découvrez comment les employés des entreprises prestigieuses maîtrisent des compétences recherchées

En savoir plus sur Coursera pour les affaires

logos de Petrobras, TATA, Danone, Capgemini, P&G et L'Oreal

Élaborez votre expertise en Data Analysis

Ce cours fait partie de la SAS Statistical Business Analyst Certificat Professionnel

Lorsque vous vous inscrivez à ce cours, vous êtes également inscrit(e) à ce Certificat Professionnel.

Apprenez de nouveaux concepts auprès d'experts du secteur
Acquérez une compréhension de base d'un sujet ou d'un outil
Développez des compétences professionnelles avec des projets pratiques
Obtenez un certificat professionnel partageable auprès de SAS

Il y a 7 modules dans ce cours

This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets. You learn to use logistic regression to model an individual's behavior as a function of known inputs, create effect plots and odds ratio plots, handle missing data values, and tackle multicollinearity in your predictors. You also learn to assess model performance and compare models.

Inclus

1 vidéo7 lectures

1 vidéo Total 1 minute

Meet the Instructor 1 minute

7 lectures Total 71 minutes

What You Learn in This Course 5 minutes
Learner Prerequisites 1 minute
Using Forums and Getting Help 5 minutes
Access SAS Software for this Course 10 minutes
Set Up Data for This Course (REQUIRED) 30 minutes
About the Demos and Practices in this Course 10 minutes
Frequently Asked Questions 10 minutes

In this module, you review the fundamentals of predictive modeling. Then you explore the business scenario data that is used throughout the course. Finally, you learn about common analytical challenges that you might encounter as a modeler.

Inclus

15 vidéos1 lecture6 devoirs

15 vidéos Total 34 minutes

Overview 1 minute
Introduction 0 minutes
Goals of Predictive Modeling 2 minutes
Terms for Elements in Predictive Modeling 1 minute
Basic Steps of Predictive Modeling 3 minutes
Applications of Predictive Modeling 2 minutes
Demonstration Scenario: Target Marketing for a Bank 2 minutes
Demo: Examining the Code for Generating Descriptive Statistics and Frequency Tables 2 minutes
Introduction 0 minutes
Data Challenges 6 minutes
Analytical Challenges 2 minutes
Separate Sampling 2 minutes
Avoiding the Optimism Bias: Honest Assessment 2 minutes
Splitting the Data for Model Training and Assessment 3 minutes
Demo: Splitting the Data 5 minutes

1 lecture Total 10 minutes

Summary 10 minutes

6 devoirs Total 75 minutes

Practice: Exploring the Bank Data for the Target Marketing Project 20 minutes
Practice: Exploring the Veterans' Organization Data Used in the Practices 20 minutes
Question 1.01 5 minutes
Question 1.02 5 minutes
Question 1.03 5 minutes
Practice: Splitting the Data 20 minutes

In this module, you investigate the concepts behind the logistic regression model. Then you learn to use the LOGISTIC procedure to fit a logistic regression model. Finally, you learn how to score new cases and adjust the model for oversampling.

Inclus

18 vidéos1 lecture4 devoirs

18 vidéos Total 54 minutes

Overview 1 minute
Introduction 0 minutes
Understanding the Logistic Regression Model 3 minutes
Constraining the Posterior Probability Using the Logit Transformation 2 minutes
Understanding the Fitted Surface 1 minute
Interpreting the Model by Calculating the Odds Ratio 3 minutes
Understanding Logistic Discrimination 2 minutes
Estimating Unknown Parameters Using Maximum Likelihood Estimation 2 minutes
Interpreting Concordant, Discordant, and Tied Pairs 2 minutes
Using PROC LOGISTIC to Fit Logistic Regression Models 0 minutes
Demo: Fitting a Basic Logistic Regression Model, Part 1 8 minutes
Demo: Fitting a Basic Logistic Regression Model, Part 2 12 minutes
Scoring New Cases 0 minutes
Demo: Scoring New Cases 8 minutes
Introduction 0 minutes
Understanding the Effect of Oversampling 1 minute
Understanding the Offset 2 minutes
Demo: Correcting for Oversampling 7 minutes

1 lecture Total 10 minutes

Summary 10 minutes

4 devoirs Total 60 minutes

Question 2.01 5 minutes
Question 2.02 5 minutes
Practice: Fitting a Logistic Regression Model 20 minutes
Fitting the Model Review 30 minutes

In this module, you learn how to deal with common problems with your predictor variables such as missing values, categorical predictors with many levels, a high number of redundant predictors, and nonlinear relationships with the response variable.

Inclus

26 vidéos9 devoirs

26 vidéos Total 76 minutes

Overview 1 minute
Introduction 0 minutes
Reasons for Missing Data 3 minutes
Complete Case Analysis 2 minutes
Methods for Imputing Missing Values 3 minutes
Missing Value Imputation with Missing Value Indicator Variables 4 minutes
Demo: Imputing Missing Values 4 minutes
Cluster Imputation 2 minutes
Introduction 0 minutes
Problems Caused by Categorical Inputs 4 minutes
Solutions to Problems Caused by Categorical Inputs 1 minute
Linking to Other Data Sets 1 minute
Collapsing Categories by Thresholding 1 minute
Collapsing Categories by Using Greenacre's Method 3 minutes
Demo: Collapsing the Levels of a Nominal Input, Part 1 6 minutes
Demo: Collapsing the Levels of a Nominal Input, Part 2 10 minutes
Replacing Categorical Levels by Using Smoothed Weight-of-Evidence Coding 3 minutes
Demo: Computing the Smoothed Weight of Evidence 5 minutes
Introduction 0 minutes
Problem of Redundancy 2 minutes
Variable Clustering Method 1 minute
Understanding Principal Components 5 minutes
Divisive Clustering 4 minutes
PROC VARCLUS Syntax 1 minute
Selecting a Representative Variable from Each Cluster 1 minute
Demo: Reducing Redundancy by Clustering Variables 9 minutes

9 devoirs Total 105 minutes

Question 3.01 5 minutes
Practice: Imputing Missing Values 20 minutes
Question 3.02 5 minutes
Question 3.03 5 minutes
Question 3.04 5 minutes
Practice: Collapsing the Levels of a Nominal Input 20 minutes
Practice: Computing the Smoothed Weight of Evidence 20 minutes
Question 3.05 5 minutes
Practice: Reducing Redundancy by Clustering Variables 20 minutes

In this module, you learn how to select the most predictive variables to use in your model.

Inclus

23 vidéos1 lecture12 devoirs

23 vidéos Total 92 minutes

Introduction 0 minutes
Detecting Nonlinear Relationships 4 minutes
Demo: Performing Variable Screening, Part 1 6 minutes
Demo: Performing Variable Screening, Part 2 4 minutes
Univariate Binning and Smoothing 3 minutes
Demo: Creating Empirical Logit Plots 10 minutes
Remedies for Nonlinear Relationships 2 minutes
Demo: Accommodating a Nonlinear Relationship, Part 1 6 minutes
Demo: Accommodating a Nonlinear Relationship, Part 2 8 minutes
Introduction 0 minutes
Specifying a Subset Selection Method in PROC LOGISTIC 2 minutes
Best-Subsets Selection 1 minute
Stepwise Selection 3 minutes
Backward Elimination 2 minutes
Scalability of the Subset Selection Methods in PROC LOGISTIC 3 minutes
Detecting Interactions 3 minutes
BIC-based Significance Level 3 minutes
Demo: Detecting Interactions 7 minutes
Demo: Using Backward Elimination to Subset the Variables 4 minutes
Demo: Displaying Odds Ratios for Variables Involved in Interactions 4 minutes
Demo: Creating an Interaction Plot 3 minutes
Demo: Using the Best-Subsets Selection Method 4 minutes
Demo: Using Fit Statistics to Select a Model 10 minutes

1 lecture Total 10 minutes

Summary of Preparing the Input Variables, Parts 1 and 2 10 minutes

12 devoirs Total 160 minutes

Question 3.06 5 minutes
Practice: Performing Variable Screening 20 minutes
Practice: Creating Empirical Logit Plots 20 minutes
Question 3.07 5 minutes
Question 3.08 5 minutes
Question 3.09 5 minutes
Practice: Using Forward Selection to Detect Interactions 20 minutes
Question 3.10 5 minutes
Practice: Using Backward Elimination to Subset the Variables 20 minutes
Question 3.11 5 minutes
Practice: Using Fit Statistics to Select a Model 20 minutes
Preparing the Input Variables Review 30 minutes

In this module, you learn how to assess the performance of your model and how to determine allocation rules that maximize profit. Finally, you learn how to generate a family of increasingly complex predictive models and how to select the best model.

Inclus

30 vidéos1 lecture9 devoirs

30 vidéos Total 78 minutes

Overview 1 minute
Introduction 0 minutes
Fit versus Complexity 2 minutes
Assessing Models when Target Event Data Is Rare 2 minutes
Demo: Preparing the Validation Data 5 minutes
Introduction 0 minutes
Understanding the Confusion Matrix 5 minutes
Measuring Performance across Cutoffs by Using the ROC Curve 4 minutes
Choosing Depth by Using the Gains Chart 3 minutes
Effects of Oversampled Data on Performance Measures 3 minutes
Adjusting a Confusion Matrix for Oversampling 1 minute
Demo: Measuring Model Performance Based on Commonly-Used Metrics 7 minutes
Introduction 0 minutes
Understanding the Effect of Cutoffs on Confusion Matrices 1 minute
Understanding the Profit Matrix 2 minutes
Choosing the Optimal Cutoff by Using the Profit Matrix 3 minutes
Using the Central Cutoff 1 minute
Using Profit to Assess Fit 0 minutes
Calculating Sampling Weights 1 minute
Demo: Using a Profit Matrix to Measure Model Performance 6 minutes
Introduction 0 minutes
Plotting Class Separation 2 minutes
Assessing Overall Predictive Power 3 minutes
Demo: Using the K-S Statistic to Measure Model Performance 2 minutes
Introduction 0 minutes
Comparing ROC Curves of Several Models" 2 minutes
Demo: Comparing ROC Curves to Measure Model Performance 4 minutes
Using Macros to Compare Many Models 1 minute
Demo: Comparing and Evaluating Many Models, Part 1 8 minutes
Demo: Comparing and Evaluating Many Models, Part 2 7 minutes

1 lecture Total 10 minutes

Summary 10 minutes

9 devoirs Total 85 minutes

Question 4.01 5 minutes
Question 4.02 5 minutes
Question 4.03 5 minutes
Practice: Assessing Model Performance 20 minutes
Question 4.04 5 minutes
Question 4.05 5 minutes
Question 4.06 5 minutes
Question 4.07 5 minutes
Measuring Model Performance Review 30 minutes

Inclus

1 lecture1 élément d'application

Obtenez un certificat professionnel

Ajoutez ce titre à votre profil LinkedIn, à votre curriculum vitae ou à votre CV. Partagez-le sur les médias sociaux et dans votre évaluation des performances.

Instructeur

Évaluations de l’enseignant

(18 évaluations)

Marc Huber

SAS

2 Cours 8 794 apprenants

Offert par

SAS

En savoir plus sur Data Analysis

EDUCBA
Logistic Regression with SAS: Build & Evaluate Models
Cours
EDUCBA
SPSS: Apply & Interpret Logistic Regression Models
Cours
EDUCBA
Logistic Regression with R: Build & Predict
Cours
EDUCBA
Predictive Analytics with SAS: Build & Deploy Models
Cours

Pour quelles raisons les étudiants sur Coursera nous choisissent-ils pour leur carrière ?

Felipe M.

Étudiant(e) depuis 2018

’Pouvoir suivre des cours à mon rythme à été une expérience extraordinaire. Je peux apprendre chaque fois que mon emploi du temps me le permet et en fonction de mon humeur.’

Jennifer J.

Étudiant(e) depuis 2020

’J'ai directement appliqué les concepts et les compétences que j'ai appris de mes cours à un nouveau projet passionnant au travail.’

Larry W.

Étudiant(e) depuis 2021

’Lorsque j'ai besoin de cours sur des sujets que mon université ne propose pas, Coursera est l'un des meilleurs endroits où se rendre.’

Chaitanya A.

’Apprendre, ce n'est pas seulement s'améliorer dans son travail : c'est bien plus que cela. Coursera me permet d'apprendre sans limites.’

Avis des étudiants

5 stars
79,36 %
4 stars
14,28 %
3 stars
0 %
2 stars
3,17 %
1 star
3,17 %

Affichage de 3 sur 63

Révisé le 10 avr. 2021

Great training sets of problems. Good guidance & teaching.

Révisé le 14 juin 2021

Thank you so much to the instructor, Michael J Patetta for teaching this course!

Voir plus d’avis

Ouvrez de nouvelles portes avec Coursera Plus

Accès illimité à 10,000+ cours de niveau international, projets pratiques et programmes de certification prêts à l'emploi - tous inclus dans votre abonnement.

Faites progresser votre carrière avec un diplôme en ligne

Obtenez un diplôme auprès d’universités de renommée mondiale - 100 % en ligne

Découvrir les diplômes

Rejoignez plus de 3 400 entreprises mondiales qui ont choisi Coursera pour les affaires

Améliorez les compétences de vos employés pour exceller dans l’économie numérique

Foire Aux Questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.