These projects highlight my ability to deliver high-impact, quantified solutions across different domains.
- Goal: Apply advanced machine learning to public safety data to improve response efficiency.
- Technical Details: Compared SARIMA, LSTM, Random Forest on a 1.3M+ row dataset. Used DBSCAN and Folium for geospatial analysis.
- Achievement: Achieved a top model accuracy of over 85% in crime trend prediction and reduced prediction error by 25% for emergency response times.
- Goal: Build a robust, comparative system for financial time-series prediction.
- Technical Details: Employed and compared models including Logistic Regression, XGBoost, RNN, and LSTM.
- Optimization: Optimized model performance by 15% through hyperparameter tuning and integrating real-time sentiment analysis.
My professional qualifications validate a strong foundation in both infrastructure and development practices.
- Cisco Certified Specialist - Enterprise Core (CCNP)
- Cisco Certified DevNet Associate (Demonstrates coding for network devices)
- Cisco Certified Network Associate (CCNA)