SeligoAI uses sophisticated algorithms enabled by Machine Learning to make predictions of student success evaluated in indices. Interaction with Machine Learning includes four main functions:
Working Dataset
Machine Learning creates a model of student success based on existing data from the students who graduated.
Model Evaluation
Machine Learning evaluates the data and checks if it is correct based on the number of fields required and quality of data.
Learning Dataset
If a model was created properly, application passes info to Machine Learning, including prospective and active students’ data.
Predictions Analysis
At the end of process the application retrieves data from Machine Learning predictions engine which is then analyzed and saved in the form of indices in a database.
