Portfolio of real-world ML projects demonstrating ranking & recommendation systems, engagement prediction, fairness, and explainability, engineered end-to-end with scalable, production-ready design principles.

ATS simulator and algorithmic bias detector for resumes. Upload your resume + a job description to see how automated hiring systems score it and detect 8 research-backed proxy discrimination patterns. Built on a peer-reviewed study — REVIZOR Journal, 2026.

Content-based niche-first recommender that surfaces Made-in-Rwanda artisan products over global brands. Multilingual TF-IDF (EN/FR/Kinyarwanda) + local-boost + fairness cap. <5 ms per query on CPU. Includes a low-tech weekly lead workflow for offline artisans.

Exploratory data analysis, Feature Engineering and Modelling on the Adult UCI Income census dataset.

This repository contains list of open-source ML Fairness repositories throughout Github. We analyzed Engagement and Maintenance of these tools

A Responsible AI system for heart disease risk assessment, featuring a Hybrid Safety Architecture and calibrated Gradient Boosting.

Official PyTorch implementation of FA-BUHS: An upstream, causal fairness-aware recommendation framework for mitigating popularity and demographic biases.

Predictive modeling, clustering, and fairness analysis on obesity-related behavioral data with public health insights.