Projects

Benchmarked the K-EmoPhone mobile and wearable dataset for in-situ emotion and stress classification. Achieved 68.3% accuracy using a soft voting ensemble of XGBoost, and TabNet models. Focused on recent sensor-based physiological and self-reported data fusion.

Built K-means and Random Forest codebooks for bag-of-words representation, developed CNN models, and benchmarked against ResNet-50 using the Caltech101 dataset.

Compared PCA, low-dimensional PCA, PCA-LDA ensemble, and Random Forest for face recognition tasks. Evaluated subspace learning methods for joint reconstruction and discrimination.

Analyzed real customs data from Korea Customs Service and built a machine learning model to detect fake import declarations with over 91% accuracy.

Developed a simple calculator using JavaScript for web basics

Implemented and visualized a Perceptron classifier in action and utilized it for spam detection, comparing its performance with traditional ML models.

Created an animated eps of a soccer player losing their leg while kicking the ball. One of my earliest coding projects, but it was fun!