Predict CHD Risk with Precision: This machine learning model analyzes patient demographics, behaviors, and medical factors to accurately predict the likelihood of developing coronary heart disease within the next 10 years.

Classification of brain diseases for IAAA Challange Dataset. This repository includes data preprocessing, model training, and evaluation workflows for image datasets, with a focus on efficient training with GPU support.

Binary classification of lumpy skin disease (imbalanced dataset) using ML algorithms in addition to oversampling/undersampling techniques.

Cardiovascular Risk Prediction - Classification

Developing a Classification Model for Predicting Credit Card Default

Does the amount of screen time a person spends at age 16 affect their levels of depression and anxiety at age 18?

DAEB-τSS3: Imbalanced Social Media Text Depression Detection Method

Multiclass ML pipeline for classifying NASA TESS exoplanet candidates from catalog features

Binary classification, SHAP (Explainable Artificial Intelligence), and Grid Search (for tuning hyperparameters) using EfficientNetV2-B0 on Cat VS Dog dataset.

EfficientNetV2 (Efficientnetv2-b2) and quantization int8 and fp32 (QAT and PTQ) on CK+ dataset . fine-tuning, augmentation, solving imbalanced dataset, etc.

2022년 1학기 개인 프로젝트 : 뇌졸증 환자 예측 모델·분석

Advanced machine learning system for detecting credit card fraud using logistic regression and statistical analysis

NLP project for multi-class mental health detection using TF-IDF + SVM and Transformer models (BERT, RoBERTa) with Streamlit deployment.

Predicting Bank Credit Card Customer Churn using the Credit Card Customers dataset.

OBEBS method

Infant cry classification using audio feature engineering and machine learning (SMOTE, XGBoost, ensemble voting) to predict likely baby needs such as hunger, discomfort, tiredness, burping, and belly pain.

This project implements a neural network-based recommendation system to predict product ratings on Amazon based on user-product interactions.

Machine learning techniques for fraud detection.

Detecting fraudulent transactions in e-commerce data provided by Vesta Corporation.