Iskolar uses Logistic Regression to score applicant eligibility and Isolation Forest to flag inconsistent or anomalous applications — helping administrators select beneficiaries fairly, consistently, and transparently.
Applicants fill out academic and socioeconomic details and upload supporting documents (income certificate, grades, indigency certificate, etc).
The system computes an eligibility probability from need-based and merit-based features — income, GPA, dependents, and more.
Applications with statistically inconsistent or outlier data are automatically flagged for manual review — reducing fraud and bias.
Applicants are ranked by eligibility score against available slots, giving administrators a transparent, defensible shortlist.
Administrators review scores, explanations, and flagged cases before approving or rejecting — the model assists, humans decide.
Applicants track their application status and final decision from their personal dashboard.