Global Stability & Risk Forecasting (GDELT)

Jan 15, 2026 · 1 min read
projects

What I built

  • Analyzed subsets of a 2PB dataset to identify global risk trends and support stability forecasting.
  • Developed and tuned a Random Forest model and validated it against historical logs.
  • Supported automated anomaly detection and data-driven recommendations.

Tools & methods

Python, SQL, Random Forest, statistical validation, anomaly detection

Outcome

A forecasting + anomaly detection workflow designed to support stakeholder-ready insights from extremely large datasets.

Data
Dhruv Saikia
Authors
Dhruv Saikia
Data | Game Dev | Cybersecurity
Master’s student at SFU specializing in Big Data.
Background in Data Science, Cybersecurity, and Game Development.
I like building big data pipelines that are secure and are user friendly.