Engineered and deployed a complete, end-to-end ELT platform using Python, Airflow, Docker, AWS S3, dbt, and Snowflake, achieving 100% automated, zero-touch ingestion and transformation of real-time API data into an analytics-ready warehouse.
Executed a strategic data warehouse migration from DuckDB to Snowflake and optimized the transformation pipeline by implementing dbt incremental models, reducing data refresh times on subsequent runs by over 90% and ensuring scalability.
Developed and integrated a Scikit-learn predictive model into a live Streamlit dashboard, providing real-time AI-driven match predictions and demonstrating full-stack capabilities from raw data ingestion to machine learning deployment.
Developed a full-stack ETL pipeline to automate the extraction of public video data from the YouTube Data API, including metadata like title, views, likes, and publish dates.
Implemented a modular architecture (extract, transform, load) using Python and pandas, saving data to a local SQLite database for storage and future analysis.
Designed transformation functions to clean and enrich the dataset by converting formats, generating video age metrics, and removing duplicates.
Robust Reinforcement Learning for Autonomous UAV Landing
Engineered a Deep Q-Network (DQN) control system in PyTorch to autonomously land a UAV, implementing the full agent lifecycle including an epsilon-greedy policy, experience replay, and a custom trainer to solve the Bellman equation for optimal throttle control.
Designed and built a high-fidelity 2D aircraft simulation in Python using the Pymunk physics engine, modeling complex aerodynamics with force-lookup tables and architecting a state representation that incorporates temporal dynamics for improved agent decision-making.
Developed a resilient learning framework by introducing a simulated communication loss module within the environment, successfully training the agent to maintain flight stability and achieve a 100% landing success rate despite receiving delayed state information.
Feed-Forward Neural Network for Aircraft Pitch Prediction
Developed a feed-forward neural network (FNN) in Python to predict aircraft pitch from flight characteristics (5400+ data points), incorporating He initialization, gradient clipping, and early stopping (patience=20) for robust training.
Optimized model performance through automated hyperparameter tuning (10+ randomized trials), identifying stochastic gradient descent with ReLU activation and a 0.05 learning rate as the superior configuration, achieving a minimum training MSE of 0.0350.
Evaluated the optimized model's generalization on unseen test data, yielding a rescaled Test MSE of 2.5609, and implemented visualization tools (loss curves, actual vs. predicted plots) for comprehensive performance analysis and reporting.
Feedforward Neural Network for Car Price Prediction
Developed a NumPy-based Feedforward Neural Network to predict used car prices, implementing a comprehensive preprocessing pipeline handling currency/unit conversions, text parsing, and engineering a 'Car Brand Region' feature from raw data (5500+ entries).
Analyzed complex feature interactions and data distributions through 13+ visualizations (histograms, scatterplots, heatmap) and compared Batch vs. Stochastic Gradient Descent, demonstrating SGD's superior convergence via RMSE tracking over 100 epochs.
Implemented feature normalization (z-score) and a prediction function capable of handling scaled/unscaled inputs, saving trained model parameters, and visualizing learning curves for performance evaluation and model persistence.
Modeling Multiple Relationships Utilizing Multiple Linear Regression with Gradient Descent
Engineered a functional online bookstore using Oracle 21c supporting user accounts, browsing, purchasing for 50+ users, and admin book management (100+ titles).
Implemented secure registration/login/logout and admin functionalities using HTML, CGI, Perl, Java, SQL.
Developed dynamic title-based search and detailed book/customer info display with clickable hyperlinks, including a "Display Source" feature for 5000+ lines of code.
User-Friendly Mobile App for Browsing and Purchasing Books
Designed and developed a full-stack web application using React.js (front-end) and Node.js/Express.js (back-end) following the MVC architecture, resulting in a scalable and maintainable codebase.
Integrated Stripe.js for online payment processing, reducing transaction errors by 20%, and implemented JWT-based authentication to ensure secure user access.
Optimized application data storage by utilizing MongoDB NoSQL SaaS solutions, improving database query performance by 25% and enhancing user experience.