Built an ANN model to predict customer churn with optimized accuracy.
Implemented an end-to-end RNN pipeline that processes movie reviews and accurately determines sentiment, showcasing deep learning in natural language processing.
Implemented an LSTM-based generative model for next-word prediction, leveraging early stopping to improve training efficiency.
Developed an end-to-end ML pipeline for phishing website detection with automated preprocessing, drift monitoring, and optimized classification models.
Developed a movie recommendation system, improving model accuracy by 20% through API integration and collaborated on algorithm refinement, achieving higher prediction precision.
Leveraged disparity analysis and predictive modeling to enable data-driven COVID-19 risk insights in Texas.
Designed a multi-agent AI architecture to simulate intelligent coordination in healthcare systems.
Developed a machine learning model to predict forest fire risk levels and guide proactive intervention.
Implemented an AI-powered simulation of an intelligent vacuum cleaner for adaptive path planning and obstacle avoidance.
Built an AI agent to optimally navigate complex mazes using reinforcement learning strategies.
Solved the N-Queens problem via local search algorithms to efficiently find valid configurations.
Developed a high-performance Connect 4 game in Python, leveraging algorithmic design and AI for competitive play.
Built a full-stack ML project with model serving, API endpoints, and end-to-end deployment orchestration.
Developed a restaurant reservation system using both Client-Server and Microservice architectures, comparing their design and performance trade-offs.