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Core Thesis Engine

Predict Graduate Employment Risk

Enter comprehensive academic, geographical, and professional profile factors to generate an algorithmic risk assessment.

Inference Latency Real-time AI Prediction
Output Engine Risk Label & Actionable Roadmap
Graduate

Profile Matrix Input

Fill in the student feature vectors to run classification models.

Live
{% if prediction_text %}
{{ prediction_text }}
{% endif %} {% if improved_text %}
{{ improved_text }}
{% endif %} {% if recommendations %}
XAI Prescriptive Recommendations:
    {% for rec in recommendations %}
  • {{ rec }}
    • {% endfor %}
{% endif %}

Simulation Strategy

This input pipeline mimics structural features within the CareerEDGE Employability Model. Tweak variables in real time to simulate interventions.

Low CGPA Anchor

Focus features on 'Weekly Development Hours' & 'Skill Type' to counterbalance academic tiers.

Null-Skill Anchor

Setting Skill Type to 'None' shifts node priority to early classification as High-Risk.

Professional Anchor

Adding 3-6 Months Internships changes the vector paths in ensemble models drastically.

Presentation Guide

During your thesis demo, emphasize these system attributes to the panel:

  • Reactive Inference: Backend processes multi-categorical structures instantly via tree-checkpoints.
  • Prescriptive Logic: The system doesn't just evaluate; it maps weak nodes to targeted corrective vectors (Recommendations).
  • Socio-Economic Weights: Demonstrates how location constraints alter job search timelines in Bangladesh.
Designed to ensure rigorous, evidence-based academic feedback.
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