Building a Machine Learning Based Recommendation Engine for the Virtual Academic Advisor System
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Machine Learning is presently being used to tackle various problems from voice recognition to self-driving vehicles. However, there are many areas where modern software applications have not reached due to lack of interest or budget. The education sector  is one of these areas, and itself poses a set of interesting questions suitable for applied Machine Learning and modern Data Analysis approaches, which can greatly benefit the community. A relatable example is the problem of a student choosing a career path, the basis of which is an appropriate academic plan. In our state, community college students have difficulty choosing a career path as they do not have a well-defined academic path to transfer to a university and major of their choice. This is due to the fact that most of the advising is done with archaic tools (if any), and faculty also often pose as academic advisors when they are already overwhelmed by their daily responsibilities. Moreover, each student has specific preferences like the choice of school, major, budget, time preference, etc., making the task of generating the study plans burdensome. Study plan creation is a form of scheduling problem, and it is not trivial. There is little research on scheduling algorithms that address the problem of finding and recommending multiple paths going from multiple starting points to multiple goals (e.g., building prerequisite networks). The goal of this research is to help community college students and advisors by implementing a Machine Learning recommendation system that automates the selection of most suitable academic plans, specifically, to transfer to four-year institutions, based on personal preferences.