Regression Modeling in R

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Section 1: Overview

  1. Course Overview
  2. How to Get Queries Resolved
  3. Download Resources

Section 2: Introduction to Linear Regression

  1. What is Linear Regression
  2. Understanding with Practical Examples
  3. Types of Linear Regression
  4. Examples of Industrial Applications
  5. Statistical Modeling vs Machine Learning

Section 3: Graphical Understanding

  1. Graphical Understanding
  2. Formulate Line of Best Fit
  3. Course Review

Section 4: Building and Interpreting Linear Regression

  1. Linear Regression from Scratch using Formula
  2. R-Squared Explained
  3. Pre-Model Build Analysis
  4. Building and Interpreting Linear Regression Models

Section 5: Measures of Goodness of Fit

  1. Problem with R-Squared
  2. Adjusted R-Squared
  3. F-Statistic, AIC, BIC
  4. R-Demo
  5. Assumptions
  6. Course Review

Section 6: Project - Linear Regression

  1. Problem Statement
  2. Handling Missing Values
  3. Outlier Analysis
  4. Graphical and Statistical Analysis
  5. Building Linear Regression
  6. Good Model
  7. Evaluation Measures
  8. Tips to Improve Model Accuracy
  9. Need for Cross Validation
  10. Cross Validation Approaches
  11. Variable Transformations and Interactions
  12. Variable Transformations and Interactions
  13. Cooks Distance for Influential Points
  14. Cooks Distance Demo
  15. BoxCox and YeoJohnson Transformations
  16. Residual Analysis
  17. Overcoming Heteroscedasticity
  18. Stepwise Regression for Model Search
  19. Best Subsets Model Search

Section 7: Gradient Descent

  1. What Exactly is Gradient Descent
  2. How Gradient Descent Learns
  3. Comparing Types of Gradient Descent
  4. Stopping Criteria and Scaling
  5. Types of Gradient Descent
  6. Course Review

Section 8: Introduction to Logistic Regression

  1. Introduction to Logistic Regression
  2. One vs Rest Strategy
  3. Use Case Examples

Section 9: Understanding the Math

  1. Math behind Logistic Regression Part 1
  2. Math behind Logistic Regression Part 2
  3. Why Negative Logloss
  4. Course Review

Section 10: Project - Logistic Regression

  1. Problem Statement Marketing
  2. Demo - EDA for Logit
  3. Building Logistic Regression Model
  4. McFadden's R-Squared
  5. Confusion Matrix and Evaluation Metrics
  6. Precision Recall Curve
  7. Optimal Cutoff Score
  8. The ROC Curve
  9. KS Statistic and Gain Curve
  10. Concordant and Discordant pairs
  11. Approaches to Handle Class Imbalance
  12. Cost Sensitive Learning
  13. Oversampling
  14. Hybrid Sampling
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