📈 Quant Market Predictor is a Python tool designed to predict stock prices from the NASDAQ

🐾 A lightweight & extensible library to create complex multi-model and multi-modal pipelines, including ``Ensembles`` and ``Meta-Models``

Building a predictive model to predict views of Ted Talks in YouTube from dataset of past events using Machine Learning models

Guidelines for the course "fundamentals of machine learning"

Python → Machine Learning | Hands-on journey with real datasets and model output charts

Datdy is an integrated research project combining Exploratory Data Analysis (EDA) and Machine Learning to optimize profitability for fashion e-commerce businesses in Vietnam. The project addresses the challenge of forecasting seasonal revenue fluctuations and proposes a data-driven 'Loss Leader' business strategy.

Bangla fake news detection using TF-IDF, classical ML models (Logistic Regression, SVM, XGBoost, etc.), and fine-tuned multilingual BERT — built with PyTorch and Scikit-learn.

Machine Learning and Deep Learning Notebooks

🤖 Predict programming problem difficulty with AI using text analysis and machine learning for accurate complexity scoring.

This is a comprehensive collection of Python implementations covering fundamental machine learning algorithms, data preprocessing techniques, model evaluation methods, and practical applications

Credit Card Fraud Detection Using Machine Learning

Built and deployed a Basketball Lineup Analytics Engine — a sports-tech decision-support tool that ranks 5-man lineups, recommends substitutions, and evaluates matchup counters.

Repo hosting the notebooks for the assignments of the fall 21 "Neural Networks and Intelligent Systems" course @ NTUA

Sentiment analysis using TF-IDF and Logistic Regression (NLP project)

The project implements multiple Machine Learning algorithms, including Logistic Regression, Decision Trees, Random Forest, and other classification techniques

Utilizing machine learning, this project employs emotion-driven models to recommend movies. By analyzing user reactions, it tailors suggestions for an emotionally engaging cinematic experience.

A collection of Python implementations covering fundamental machine learning algorithms, preprocessing techniques, regression, classification, clustering, evaluation metrics, and practice problems.

Machine Learning Classification Model. Developed a machine learning model to predict student performance (pass/fail) based on academic and behavioral features.