Purpose
A group project for an introductory computer science course (CAPP 30122) aimed at creating a tool to help prospective students find cusotmized cost-benefit information about enrollment in higher education protrams. The tool analyzes and compares the cost of higher education programs to predicted wages in related job titles.
Methods
The code is written in Python and uses Beautiful Soup to extract job requirements and wages from O*NET and college costs from US World News college rankings.
Costs and outcomes are run through a sklearn linear regressive model to allow granular education/location/experience predictions.
The Invest in U web interface.
The web interface is supported by a flask framework.
Results
Given user background input (intended field, highest education, work experience, state to work in), we sucessfully return:
- Expected annual wage with the current background, calculated by our model.
- Expected hourly wage and annual wage with educational level increased to the Master’s level.
Due to project time constraints we where unable to:
- Better optimize the prediction model.
- Expand educational predictions for multiple degree levels.
- Provide specific college recommendations due to failure to debug that module.
