Educational Bias

This project examines a dataset that predicts students’ academic success based on more than 4500+ entries and 37+ variables. According to the associated text, the data collected is from multiple disjoint sources from the Polytechnic Institute of Portalegre database. Based on this data, a machine learning model was trained on past students that had already graduated or dropped out to predict academic outcome. Some of the variables included are personal characteristics, such as race or gender.

This project analyzes how the Student Performance Dataset, constructed of high school students' personal information and GPA based variables, is displayed as numerical values.