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.

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

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

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

rsna_pneumonia_project

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.

2024 한국인공지능융합기술학회 추계학술대회에 제출한 논문에 대한 연구 내용입니다.

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.

Hourly Energy Consumption

A machine learning credit scoring

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.

A comprehensive framework for developing and backtesting quantitative trading strategies.

Credit risk prediction using machine learning, ensemble modeling, Optuna optimization, SHAP explainability, fairness analysis, and interactive deployment.

CommentSense v1: Fine-tuned Twitter-RoBERTa transformer on 1M+ YouTube comments, deployed with FastAPI and Hugging Face, and integrated into a Chrome Extension for real-time sentiment analysis.

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.

Predicting the probability of which a Formula 1 driver will pit on the next lap using race telemetry data.

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