Featured Project

Automate Research Data Collection with LLM Intelligence

Transform months of manual data entry into hours. Extract entities, structure events, and build quantitative datasets from qualitative sources — powered by LLMs and AI Agents, and designed for researchers.

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Technology Stack

Python LLMs API Integration MCP Web Scraping NLP

Why Researchers Choose This Solution

Dramatically Reduce Data Collection Time

Automate the manual work of extracting data from hundreds of documents. Focus on analysis, not data entry.

Automatic Entity Recognition

Define and extract custom attributes and variables from unstructured text sources using LLM-powered parsing for flexible data structuring.

Process Multiple Sources Simultaneously

Collect data from news articles, reports, academic databases, and policy documents in parallel.

Maintain Research Standards

Full methodology transparency and reproducible workflows that meet academic research requirements.

Structure Unstructured Data

Convert qualitative text into quantitative datasets ready for statistical analysis.

Validated Output Quality

Built-in quality checks and validation ensure data accuracy and consistency.

More Projects

Live Application

PRO-TEST v2.0: Protest Outcome Prediction

A full-stack ML system that predicts likely security responses to protests in Iraq, Lebanon, and Egypt. Trained on 13,000+ historical protest events, the ensemble model forecasts outcomes including verbal coercion, detention, and physical force — served via a REST API with a live interactive frontend.

Python FastAPI Next.js TypeScript XGBoost LightGBM Random Forest Redis
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13,000+ protest events
Ensemble model (RF + XGBoost + LightGBM)
Iraq, Lebanon & Egypt coverage

Production system with 83-test CI/CD pipeline, Prometheus + Grafana monitoring, and separate backend API hosted on Render.

Interested in Research Collaboration?

Please feel free to get in touch to discuss potential opportunities or view my work in detail.

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