Statistics @ Ewha Womans University

Analyzed equipment-level power patterns and forecast electricity demand using K-means clustering and an ensemble of cluster-level and process-wide forecasts.

Combined travel survey and transportation data to build an infrastructure index, predict solo-traveler spending, and explore improvement scenarios for tourism policy.

Predicted daily stock movements in five categories using pre-market information and historical return distributions, with checks on input timing and consistency after code changes.

Implemented high-dimensional volatility matrix estimation, decomposition, and forecasting in Python using one-minute data for 200 S&P 500 stocks during a POSTECH FBDA Lab internship.

Predicted pitch control success probabilities using pre-pitch game information and historical records. Team rank: 94/1,087 (top 8.6%).

Project documentation is primarily in Korean.

Python · R · SQL · Power BI

• Ewha Statistical Analysis Association — ESAA study and project archive
• Ewha Statistics Coursework — Statistical analysis and visualization in R

Data Analytics · Statistical Modeling · Machine Learning · Business / Consulting Analytics