US College Net Price Prediction Comparing ML Regression Models

TL;DR

This paper compares various machine learning regression models to predict the net prices of US colleges using government-published data, aiming to improve accuracy and fairness in cost estimation.

Contribution

It introduces a comparative analysis of multiple ML regression models for predicting college net prices based on US College Scorecard data.

Findings

Different models' prediction accuracies are evaluated.

The study provides insights into the most effective ML models for this task.

Results highlight the potential for ML to inform students and policymakers.

Abstract

This paper will illustrate the usage of Machine Learning algorithms on US College Scorecard datasets. For this paper, we will use our knowledge, research, and development of a predictive model to compare the results of all the models and predict the public and private net prices. This paper focuses on analyzing US College Scorecard data from data published on government websites. Our goal is to use four machine learning regression models to develop a predictive model to forecast the equitable net cost for every college, encompassing both public institutions and private, whether for-profit or nonprofit.