Data Scientist focused on Sports Analytics, Local Tourism, AI Safety, and Supply Chain Optimization.
Featured Project
Leverages core datasets and cutting-edge AI models to streamline and enhance the accuracy of
NBA game predictions.
• Data Management:
Processes and stores play-by-play data from the NBA Stats API in an optimized SQLite
database.
• Prediction Engines:
Utilizes models like Ridge Regression, XGBoost, and MLP, with ongoing development of a
custom deep learning and GenAI engine for superior accuracy.
• Web App: Delivers a
user-friendly platform for accessing game predictions and scores, offering seamless
interaction and real-time updates.
Past Work
Using data analytics and machine learning to create a comprehensive and profitable system
for predicting the outcomes of NBA games.
• Data acquisition architecture that
leverages Scrapy, Airflow, and RDS Postgres to analyze and store data on NBA teams,
players, and games from a diverse range of data sources.
• Data modeling setup employing AutoML for
quick iteration and Deep Learning Transformer models for optimized performance.
• Public-facing web application and
dashboard to showcase predictions and results.
Predicting Yelp Review Quality
Utilizing the Yelp Open Dataset, this project predicts review quality to enhance user engagement and satisfaction. It leverages Apache Spark for ETL processing and AWS RDS for database hosting, while incorporating advanced feature engineering techniques and machine learning models. Through text analysis and sentiment analysis, it offers improved insights into user behavior and drives data-driven decision-making on Yelp.
In this project, I implemented machine learning and natural language processing techniques to predict fraudulent events from transaction data. The results were visualized through an intuitive Flask web application, deployed on AWS. This project highlights my ability to transform complex data into actionable insights.
AP Staffing - Optimizing Distribution Center Operations
Implemented data-driven analysis to develop an efficient employee scheduling system that effectively balances consistent and fluctuating work demands. Achieved substantial cost savings aligned with the company's evolving objectives, simultaneously enhancing customer satisfaction, minimizing employee stress levels, and mitigating associated quality concerns.