SaleFore AI: Ultra-accurate sales forecasting using ensemble ML (XGBoost, LightGBM, CatBoost) with RTX 4060 GPU optimization. Achieves 88-95% accuracy with advanced hyperparameter tuning.

Understanding menstruation and cycle length using clustering, predictive modeling and model interpretability

rsna_pneumonia_project

A reinforcement learning trading agent that uses Proximal Policy Optimization (PPO) with automated hyperparameter tuning via Optuna to learn optimal trading strategies.

Hourly Energy Consumption

Banking_ML_Project

This project was developed for the ML Engineering Postgraduate Program, where a classification machine learning model was built to predict whether a customer will subscribe to a term deposit after a marketing campaign.

Predicting telco customer churn with deep learning and advanced feature engineering on the Telco Customer Churn dataset.

2024 한국인공지능융합기술학회 추계학술대회에 제출한 논문에 대한 연구 내용입니다.

This repository contains a comprehensive deep learning solution for Alzheimer's Disease Classification using state-of-the-art DenseNet architectures optimized with Optuna hyperparameter tuning. The project implements multiple DenseNet variants for classification of Alzheimer's disease stages from brain MRI images.

This project explores Attention-Based Transformer Encoders to develop robust buy/sell classification models for financial time series. It addresses market non-stationarity and noise by combining De Prado-inspired preprocessing with a hybrid Transformer-LSTM architecture.

Leveraging XGBoost to predict whether a customer will subscribe to a bank's term deposit

A Multimodal Regression Pipeline that predicts property market value using both tabular data and satellite imagery.

AI-powered anemia detection with classical ML, refined datasets, and explainable predictions using SHAP.

Kaggle Playground Series - Season 5, Episode 5

The final structure of my thesis project (notebooks and files still needs some polishing).

This study proposes a deep learning-based object detection framework utilizing YOLOv11 to automate the identification and classification of three common dental lesion types which are caries, gingivitis, and white spot lesions, using high-resolution intraoral photographic images.

Loan default prediction notebook using traditional machine learning models and LightGBM. Tackling imbalanced financial data and evaluating performance with ROC-AUC.

A curated collection of machine learning and deep learning notebooks — classification, regression, CV, autoencoders, NLP, and time series forecasting with TensorFlow, PyTorch, and Ray Tune.

Création d'un pipeline pour prévoir la consommation électrique d'une presse à balle