• 👋 Hi, I’m @thyphan2025
• 👀 I’m interested in AI & Machine Learning.
• 🌱 I’m currently pursuing Master of Science in Data Analytics Engineering at George Mason University
• 😄 Pronouns: she/her
• ⚡ Fun fact: I love exploring different cultures, especially their amazing foods.
• ⭐ Motivation quote : "I have no special talents. I am only passionately curious." - Albert Einstein

• Data Analytics Project (Capstone)
• Building small passion projects to explore data workflows and new tools
• Reading Designing Machine Learning Systems by Chip Huyen
• Reading Machine Learning Systems by Prof. Vijay Janapa Reddi - Harvard University
• Reading Fairness and Machine Learning by Solon Barocas, Moritz Hardt, Arvind Narayanan
• Starting MLOps Zoomcamp course

Python, PySpark, Databricks

• Cleaned and reshaped a multi-state bridge dataset to examine material and design patterns and applied association rule mining to identify recurring relationships.

→ Bridge-Material-and-Design-Analysis

Power BI

• Explored multi-season influenza data to monitor trends, subtype distribution, and outbreak severity through an interactive dashboard.

→ Influenza Surveillance Dashboard Chicago

R, Time-Series Analysis, Interactive Plot, Forecasting

• Cleaned and analyzed multi-year air quality data to examine environmental risk patterns and forecast ozone trends using ARIMA model.
• Published interactive HTML report with code, Plotly visualizations, and a few static plots.

→ New York Air Quality Analysis

Python, Data Analysis, Machine Learning

• Analyzed electric vehicle adoption data to examine growth trends, geographic distribution, and vehicle characteristics across regions.
• Trained a Decision Tree Model to classify between Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs)
• Utilized Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance.

→ Electric-Vehicle-Analysis

Python, SQL, R, NLP

• Cleaned and analyzed global incident data to identify geographic hotspots, severity patterns, and recurring risk signals affecting education infrastructure.
• Applied natural language processing (NLP) to extract sentiment and patterns from incident descriptions.

→ Education-in-Danger-Incidents

Python, PySpark, Spark MLib, Databricks

• Contributed code to the PySpark modeling workflow in Databricks, including feature engineering and evaluation using Python, PySpark and Spark MLlib.

→ Bridge-Damage-Prediction