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

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

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

Kaggle Playground Series - Season 5, Episode 7

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

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

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.

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.

Kaggle Playground Series - Season 5, Episode 5

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

A comprehensive framework for developing and backtesting quantitative trading strategies.

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! 🥈