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EasyVisa Certification Intelligence

Office of Foreign Labor Certification (OFLC) — Ensemble Classification

What you'll see here

Predict the case_status (Certified / Denied) for 25,480 historical visa applications so OFLC analysts can prioritise cases and apply consistent, evidence-backed decision criteria.

Business Context

The Office of Foreign Labor Certification (OFLC) processes employment-based immigration certifications on behalf of the US Department of Labor. Under the Immigration and Nationality Act (INA), employers must show that hiring a foreign worker will not adversely affect US workers' wages or working conditions.

EasyVisa is the OFLC's data partner. Case volumes have grown to the point where manual review is a bottleneck. A well-calibrated classifier can (a) auto-prioritise low-risk cases for fast certification, (b) flag high-risk cases for detailed review, and (c) surface the applicant and employer attributes that most drive outcomes.

Business objective

Reduce time-to-decision without sacrificing decision quality, and provide statutory transparency into what drives certification.

Classification objective

Predict case_status (binary: Certified vs Denied) from applicant, employer, and wage attributes.

Decision the model supports

Which cases to route to fast-track certification, standard review, or heightened scrutiny queues.

Target variable
case_status
— binary target (positive class: Denied)
Certified · 66.8%
Application approved for labour certification.
Denied · 33.2%
Application rejected — the positive class we predict.
AI: Why this classification problem matters

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