Data enthusiast from Brooklyn, New York, with a background in Psychology and Research. Proficient in Python, Excel, Tableau, and SQL, highly motivated and analytical with a strong foundation in data analysis/visualizations, machine learning, and statistical modeling.
- 🔭 Currently working on adding projects to my repo and searching for a position in Data.
- 📈 Committed to continuously expanding my knowledge in the evolving field of data science.
- 👨🏿💻 My projects are available at my Data Science & Analytics Portfolio
- 💬 Ask me about Data Science, Music, Film & TV/Anime, and/or Soccer/Basketball!
Here are some projects that I'm particularly proud of (WIP = Work in Progress):
The 2024-2025 NBA season is just starting and you have just landed a job as a data scientist for your favorite NBA team. With various changes in play style, officiating, and general strategy things look very different for professional basketball than when the NBA first started. In this project, we hope to determine different player “archetypes” which you can think of as types of roles that are not concretely defined.
The task is to train a machine learning model that can automatically generate answers to written questions a user inputs. For this purpose, a model will be trained with questions and answers using the Python Questions from Stack Overflow dataset.
Contributed research to the company's infrastructure, with the goal of training a deep learning model using BERT to predict user geolocation from individual tweets. Yachay is an open-source Machine Learning community that has collected decades worth of useful natural language data from various sources.
Tasked with developing a Python-based regression model to predict the valence of pop songs for playlist curation and other applications. Valence describes the musical positiveness of a track, ranging from sad/depressed to happy/cheerful. An automatic method of classifying the valence of pop songs is useful for playlist curation and other applications.
