How it works?
Data Upload
Upload a CSV file containing detailed information about students. This file should include various parameters such as grades, extracurricular activities, and personal statements that will be analyzed by our algorithm.
Data Segmentation
The algorithm divides the student data into two distinct sets. One set is used to train our neural network, teaching it to recognize patterns and factors that contribute to successful admissions. The second set is reserved for making predictions.
Prediction and Evaluation
Once training is complete, the algorithm evaluates the second set of data. It identifies two groups: one comprising students expected to be admitted (stable predictions) and the other containing students whose admission status is uncertain (predicted admissions).
Admission Recommendations
Based on the algorithm’s predictions, universities receive a list of recommended students for admission. This list helps institutions make informed decisions, aligning student capabilities and potential with their admission criteria.
