This project extracts the content from computer science university course syllabi, applies NLP processing to the content, connects to ConceptNet for ,apriori association rule mining
Predict how much academic rigor is require to be successful in a computer science course at East Tennessee State University given the course syllabus and basic characteristics of the course conditions
Determine the impact of dataset expansion via ConceptNet for increasing relationship identification within the dataset
Evaluate how courses are ranked and grouped after supervised prediction, because target values are subjective therefore the results are subjectively interpretable
Extract content from computer science university course syllabi in a variety of formats with accuracy via PDF Plumber, Pytesseract, and standard text cleaning
Cluster courses to fill in missing target values for the sake of training
Apply NLP processing to identify important terminology in individual documents
Use ConceptNet to expand the corpus content for increasing relationship potential among documents
Apply Apriori association rule mining to quantify relationships among documents within corpus
Construct feature set fit for supervised learning via MLP
Predict "quantity" of academic rigor required to be successful in a computer science course at East Tennessee State University (from a scale from 0 to 100 based on instructor self rankings) through MLP evaluated using Leave One Out Cross Validation (LOOCV) for context preservation