Project Title: Creating a Recommendation Engine to Output College Programs Based on Interests and Grades
BASIS Advisor: Dean Lizardo
Internship Location: Contiq Inc.
Onsite Mentor: Arun Lal, Co-Founder
In the college application process, a student chooses a list of colleges to apply to because they see a fit for themselves on campus. However, there are far too many universities (5,300) and aspects of each university that a prospective student can experience or has exposure too. Additionally, a student probably does not have the bandwidth to find all the information that is provided on the internet, again causing a lack of information. CollegeProRec is a program geared towards solving this issue, as the program will use the vanguard of computer science- machine learning – to recommend users a list of college programs that fit their profile. Through methods such as Bayesian algorithms and decision trees, the program will use past data scraped from numerous data points such as AdmitSee and Naviance to mold a realistic list of colleges. The goal of this project is to create a viable algorithm that can recommend college programs from a conference of universities (Pac-12, ACC, etc.) to a user profile. I hope to discover the intricacies of machine learning algorithms and also better quantify the seemingly very subjective college process in order to better applicants' chances of college admission.