OxiDiviner: A production-ready, open-source Rust library for time series analysis and forecasting, especially for financial markets. Features a wide array of models including ARIMA, GARCH, ETS, Kalman Filters, Markov Regime-Switching, and more. Offers multiple API layers for all expertise levels.
Jopara (Guarani-dominant mixed with Spanish) sentiment analysis corpus
Repository for systematic trial-and-error tuning of traditional machine learning models (Logistic Regression, SVM, Ensemble, etc.) without external libraries. Focused on improving validation accuracy through manual feature engineering, parameter adjustment, and custom implementations.
Automating medical diagnosis support: A machine learning pipeline and web interface that analyzes 3D brain MRI scans to accurately distinguish between Multiple Sclerosis and Cerebral Small Vessel Disease.
Geleneksel Makine Öğrenmesi Yöntemleri ile Çalışmalarım
2110446 Data Science and Data Engineering (2022/2) - Project
Navigating technical interviews, I found many resources on basic algorithms either too complex or overly simplistic. As a machine learning engineer, blending algorithmic concepts with ML is key. This repo aims to balance complexity and practicality, making algorithmic challenges approachable and engaging.
Handwritten Digit Classification on MNIST Dataset, Utilising Only Traditional Machine Learning Techniques and a Custom Feature Extractor, achieving highest accuracy of 98.08% with the same.
A data science project analyzing NYC taxi trip durations using PySpark, from January to June 2019, including data preprocessing, geospatial visualization, and predictive modeling.
Pushing things as I do data science stuff
My code for practicals (or projects/labs) of course Machine Learning at Oxford.
Built an AI-powered outbreak prediction system using multi-domain health, mobility, and environmental data. Trained and evaluated multiple models, with XGBoost achieving 85.1% accuracy. Integrated SHAP and LIME for explainability and developed interactive D3.js dashboards with KPI cards for transparent public health decision support.
This project explores the usage of machine learning techniques in image denoising, particularly ridge regression and dictionary learning. It also includes an implementation of a readily runnable python script for capturing and denoising an image
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