Introduction to Predictive Modeling in R

This is solid workshop that can be both taught as a stand-alone workshop or as part of the R track on the digital institutes.

Credits

This workshop was written by Yuxiao Luo and adapted from a wonderful R workshop by Víctor Peña.

Workshops

The Introduction to R & RStudio (adapted from Intro to R Workshop) is a preliminary workshop for predictive modeling in R and was first taught at CUNY GCDI by Yuxiao Luo on 10/01/2021 as a 1.5 hours online synchronous workshop.

The first part PredictiveModeling1 was first taught at CUNY GCDI by Yuxiao Luo on 03/19/2021 as a two hour online synchronous workshop.

Abstract:

In this workshop, we will learn the basics of supervised learning (predictive models), including regression (predictive continuous outcomes) and classification (predicting categorical outcomes. We will go through the modeling pipeline, including data pre-processing, model fitting, and performance assessment. Some introductory R knowledge is necessary. As we don't have time to go over the basics durin the workshop, you can go to another GCDI R workshop (https://github.com/GC-DRI/R) and pick up the basic R in 2-3 hours before coming to this one. For the 1st part, you can find the materials in folder PredictiveModeling1_classification. The R script is PreModWorkshop1.R and dataset is stored in the folder data. The tutorial markdown file is PreModWorkshop.md.

Requirements

• Students need to be familiar with R. We should suggest them to take the Intro to R workshop first.
• Students should install R and Rstudio on their computers following this installation instruction.
  • Alternatively, students can use RStudio Cloud, which runs in the browser and require no installation.
• It would benefit students if they came to this workshop with some basic notions of how RStduio works. Any 5-10 minute long tutorial on Youtube should be enough for that. Whoever is leading the workshop should give them the link.

Reception and Feedback

The first Predictive Modeling workshop receives positive feedback and students have interests in it. More feedback will be collected and summarized here in the future.

Needed/Desired Changes

The current version of the Predictive Modeling workshop has no challenges/exercises. May add some in the future.