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

End-to-end ML project predicting NYC taxi fares using XGBoost + Optuna on a 33M row dataset | R² = 0.9851 | MAE = $0.66

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.

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.

Hourly Energy Consumption

2024 한국인공지능융합기술학회 추계학술대회에 제출한 논문에 대한 연구 내용입니다.

This project implements a **Handwritten Digit Classification** system using the **MNIST dataset**. The model is trained to recognize digits from `0–9` based on grayscale images of handwritten characters. The project demonstrates the application of deep learning techniques for image recognition tasks.

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

This project implements a Fashion MNIST Classification system using the MNIST dataset. The model is trained to recognize Fashion objects like shirts,shoes,trousers etc. based on grayscale images of clothes. The project demonstrates the application of deep learning techniques for image recognition tasks.

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

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

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.

Kaggle Playground Series - Season 5, Episode 5

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.

Predicting student exam scores using LightGBM and CatBoost with advanced feature engineering | Kaggle Playground Series S6E1 | RMSE: 8.73

A machine learning credit scoring

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.