ISLR Chapter 9 - Support Vector Machines Problems 5, 8, and 9
Chapter 7: Tree-Based Models, Random Forest, and Gradient Boosted Models with the Carseats and OJ Datasets
Chapter 7 - Moving Beyond Linearity with Polynomials & GAM
Best Subset, Ridge Regression, Lasso, PCR, and PLS
Resampling Methods - Validation Set, Cross Validation and Bootstrapping
Classification Methods: Logistic Regression, LDA, QDA, and KNN Analysis (Chapter 4)
Assignment 2: Linear Regression (Chapter 3)
School District Data that displays the # of English Learners per district
Working with Leaflet to map graffiti locations in R.
Working support vector machine models to classify outcome variables.
Performing decision tree analysis and making predictions.
Working with predictive analysis through polynomial regression and step functions using cross validation. Additionally fitting GAMs to help understand regression.
Explores regression methods such as Best Subset Selection, PCR, PLS, Ridge, and Lasso
Answers questions 3, 5, 6, and 9.
Answers questions 10, 11, and 13
Answers questions 2, 9, 10, and 12
Assignment: Create a web page that features a map created with Leaflet. You can use R markdown. Host your webpage on RPubs. Your webpage must contain the date that you created the document, and it must contain a map created with Leaflet.