I translate behavior-adjacent data into actionable insights by combining quantitative analysis with UX research thinking. I am particularly interested in roles involving user behavior, experimentation, and data-informed product decisions.
- 🏫 Undergraduate @ Chung-Ang University
- 📚 Majoring in Psychology, English Literature, and Technology-Art
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🧠 Behavioral Science
Cognitive psychology, user behavior analysis, experimental thinking -
📊 Data Analysis
Python, EDA, visualization, statistical modeling, validation -
🎨 UX & Creative Tech
UX research, Figma prototyping, creative coding (Processing)
| Project | Tech Stack | Key Outcome |
|---|---|---|
| 📺 Netflix Originals Hit Analysis Structural Drivers of Audience Engagement |
Python, Pandas, Scikit-Learn, TMDb API | Analyzed ~2,000 Netflix Original series to examine how release timing, genre composition, and country of origin relate to audience engagement outcomes, using leakage-aware modeling (ROC-AUC ≈ 0.83). |
| 🎧 Sonic Hit Predictor AI Audio Analysis Dashboard |
Streamlit, Librosa, Random Forest, Plotly | Built an interactive web app that extracts audio features (BPM, energy) to estimate popularity and provide actionable optimization feedback. |
| 🍎 Apple Music Data Analysis Song Success Predictor Model |
Python, Statsmodels (OLS), Pandas, Spotify API | Quantified “hit potential” of unreleased tracks using multivariate regression on metadata and historical performance. |
| 🎵 Spotify Popularity Analysis Pure Audio Model |
Python, Random Forest, Linear Regression, Spotify API | Analyzed 6,000+ songs to examine recommendation bias; a momentum-based model reduced RMSE by 15%. |
| 🎪 University Festival Web Log Analysis LUCAUS 2025 |
Log parsing, traffic pattern analysis | Identified user drop-off points via server logs and introduced contextual CTAs to improve navigation and retention. |
