Predictive Modeling

This page contains links to html renders of RStudio notebooks that I developed in Fall 2019 for the following two courses offered at Maryville University:

• DSCI 412 - Predictive Modeling (Undergraduate)
• DSCI 512 - Predictive Modeling (Graduate)

These courses introduce the theory of several machine learning and statistical learning techniques. They also cover the use of R for performing tasks related to the techniques.

• 1.1 - Introduction to Statistical Learning
• 2.1 - Introduction to Supervised Learning
• 2.2 - Training Regression Models
• 2.3 - Bias-Variance Trade-Off
• 3.1 -Simple Linear Regression
• 3.1.a - Derivation of Parameter Estimates
• 3.1.b - Derivation of R-Squared
• 3.2 - Assumptions about Error Term
• 3.3 - Inference in SLR
• 3.4 - Multiple Regression
• 3.5 - Categorical Predictors
• 3.6 - Logarithmic Transformations
• 4.1 - Logistic Regression
• 4.2 - Multinomial Logistic Regression
• 4.3 - KNN Classification
• 5.1 - Introduction to Cross Validation
• 5.2 - Introduction to Caret
• 6.1 - Ridge and LASSO Regression
• 6.2 - Regularized Logistic Regression
• 8.1 - Decision Trees
• 8.2 - Random Forests
• 10.1 - Principal Component Analysis
• 10.2 - PCA for Facial Recognition