Graduation Rate Analysis

This project investigates factors influencing college graduation rates using the College dataset from the ISLR R package. It was completed as part of the STA141A: Fundamentals of Statistical Data Science course at UC Davis.

We applied both supervised learning (linear regression) and unsupervised learning (hierarchical clustering and principal component analysis) techniques to explore which attributes most significantly impact graduation rates.

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Research Questions

1. Which attribute has the most impact on the graduation rate? How does it impact it?
2. Is a linear regression model a good fit for the data?
3. Do linear regression and unsupervised learning lead to consistent insights?

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Requirements

You will need:

• R (version ≥ 4.0 recommended)
• R packages:
  • ISLR
  • corrplot

Install required packages in R:

install.packages("ISLR")
install.packages("corrplot")