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

2024 한국인공지능융합기술학회 추계학술대회에 제출한 논문에 대한 연구 내용입니다.

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

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

Kaggle Playground Series - Season 5, Episode 7

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

Kaggle Playground Series - Season 5, Episode 5

A modular AutoML framework for text classification using the IMDB dataset. The project compares CNN and RNN architectures for sentiment analysis and leverages Optuna for hyperparameter optimization. Built with TensorFlow/Keras, the pipeline is designed to be reusable, and extensible.

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

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

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 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.

Hospital Readmission Prediction Challenge - We participated in the SOFTEC'25 Machine Learning Competition organized by FAST-NUCES Lahore and secured 2nd position — with just a 0.0050 accuracy margin from the 1st place! 🥈

A comprehensive framework for developing and backtesting quantitative trading strategies.

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.