An almost comprehensive project comparing and analyzing basic machine learning methods on a moderate-size dataset with 12 continuous inputs and two discrete inputs.
- R programming language
- Tidyverse library
- Caret library (included in Tidyverse library)
- Other libraries specific to different parts of project
- Simple Linear Regression (with basis functions)
- Regularized Regression with Elastic net (pair and triplet interactions)
- Logistic Regression (Generalized Linear Regression)
- Neural Network (single hidden layer, regression and classification)
- Random Forest (regression and classification)
- Gradient Boosted Tree (regression and classification)
- Support Vector Machines (regression and classification)
- Multivariate Additive Regression Splines (MARS) (regression and classification)
- Partial Least Squares