I build high-impact data solutions across computer vision, time-series forecasting, NLP, and more. Below are a few highlights:
<img src="https://media0.giphy.com/media/v1.Y2lkPTc5MGI3NjExYnBlZ3diMTU3ZG42YXJ2NjduMDU3c3RqM3R4MDlzNWhnNTg0YnFqNyZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/i45P7BemKpvpu/giphy.gif" alt="Pokemon" width="200" src="https://media0.giphy.com/media/v1.Y2lkPTc5MGI3NjExMXU2b3VhaHN2eW81Z2J4cnNtZng4NHhqcHRnY3VrY3VvMmFpcmw0byZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/139eZBmH1HTyRa/giphy.gif" alt="Pikachu dancing" width="200" />
Image Classification Project Transfer-learned MobileNetV2 in Colab to classify ten custom image categories—reaching 91% validation accuracy in under 20 epochs, with real-time training visualization and experiment tracking via Weights & Biases.
Time Series Analysis & Forecasting Engineered windowed LSTM models to predict next-day rainfall across multiple stations—achieving a 0.48″ RMSE and R² = 0.86 on held-out data, outperforming persistence baselines by over 20%.
Interpolation_Outlier_Detector_SQL Designed a three-stage PostgreSQL pipeline using advanced window functions and CTEs to auto-fill 100% of sub-hour gaps and flag critical ±3σ anomalies, streamlining data integrity workflows for real-time sensor networks.
Stock news Sentiment Analysis Built an end-to-end FinBERT pipeline that scrapes financial headlines, quantifies sentiment, and backtests trading signals—generating 12.5% annualized returns vs. 6.3% buy-and-hold, with automated report generation. STILL IN PROGRESS
Regression Exploration Conducted a full EDA and regression study using Linear, Ridge, and Lasso models on real-world data—engineering polynomial features to hit R² = 0.78 and RMSE = 2.4, complete with interactive visualizations.