license: other
license_name: proprietary
license_link: LICENSE
language:
- en
pipeline_tag: tabular-classification
metrics:
- accuracy
tags:
- education
FutureGuard Student Dropout Risk Prediction Models
Overview
This repository contains the trained machine learning artifacts used by FutureGuard, a student dropout risk prediction system designed for educational institutions. The models are used as part of a backend ML microservice and are not intended to be used standalone without the FutureGuard application stack.
The primary goal of these models is to estimate the probability of a student dropping out based on academic, attendance, and administrative indicators, and to support early intervention workflows.
Models Included
This repository provides:
Tabular classification model
- Algorithm: Gradient-boosted decision trees (XGBoost)
- Output: Dropout risk probability
Preprocessing artifacts
- Feature scaler used during training
- Fixed feature ordering required for inference consistency
The models are consumed dynamically by the FutureGuard ML service at runtime.
Intended Use
The models are intended to be used for:
- Academic risk analysis
- Early identification of at-risk students
- Supporting mentoring and counseling workflows
- Institutional analytics and reporting
They are designed to operate in conjunction with:
- Rule-based risk checks
- Structured metadata-driven feature mapping
- A FastAPI-based inference service
Input & Output
Input
- Structured tabular data representing student attributes
- Features must match the expected schema and preprocessing pipeline
Output
- Dropout risk score (probability)
- Risk category (low, medium, high)
- Used downstream for explanations and recommendations
Integration
These models are loaded at runtime by the FutureGuard ML Service, which:
- Downloads the model artifacts securely
- Applies preprocessing and scaling
- Combines ML predictions with rule-based logic
- Exposes a single
/predictAPI endpoint
Direct inference from this repository is not supported.
License
This repository and all included model artifacts are released under a proprietary license.
- Copyright © 2025 Manpreet Singh and Sumit Kumar
- All rights reserved
- Use permitted as-is
- Modification, redistribution, resale, or derivative works are prohibited without explicit permission
See the LICENSE file for full terms.
Disclaimer
The models are provided as-is, without any warranties. Predictions should be used as decision-support signals and not as the sole basis for academic or administrative actions.