- Description: An NLP-powered market sentiment web app that aggregates financial news, extracts full articles, classifies narratives with a custom ML model, and visualizes how media coverage shapes a company’s stock performance in real time.
- Note: I am actively improving the model’s accuracy and refining its heuristic and rule-based adjustments for better real-world performance.
- Website Link: Stock Detector
- Description: Convert text or webpage content into brief, meaningful insights.
- Website Link: Text Summary
- Description: Website for a U.S.-based e-commerce and Amazon consulting company showcasing services, brands and private label products.
- Website Link: KatamaLLC
- Description: Website for a luxury restaurant concept showcasing its menu, brand aesthetic, and boutique imagery.
- Website Link: The Dream Suite
- Description: Website for a construction company showcasing their projects.
- Website Link: Building Company Website
- Description: This project implements lossless compression algorithms: Huffman and RLE and measures how their performance changes when parallelized using a hybrid OpenMP + MPI model. MPI distributes file chunks across processes, while OpenMP accelerates compression inside each process. Throughput, timing, and scalability are compared against sequential baselines.
- Performance Summary: (1 MB File) RLE: 30.9 MB/s → 550–580 MB/s (≈ 19× faster) Huffman: 0.12 MB/s → 4.35 MB/s (≈ 35× faster)
- Performance Summary: (4 MB File) RLE: ~28.9 MB/s → 320–340 MB/s (≈ 11×–12× faster) Huffman: ~0.11 MB/s → 3.9–4.1 MB/s (≈ 35×–37× faster)
- GitHub Repository: Compression Algorithms