Fit interpretable models. Explain blackbox machine learning.

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

A game theoretic approach to explain the output of any machine learning model.

Boosted Hybrids of ensemble gradient algorithm for the long-term time series forecasting (LTSF)

Supporting code for the paper "Finding Influential Training Samples for Gradient Boosted Decision Trees"

Forecasted Airbnb 'Super host' status in Chicago with an 84% accuracy using Logistic Regression and assessed potential returns on investment employing the Herfindahl Index for strategic investment insights

Analyze the impact of COVID-19 on Airbnb bookings in Chicago and Boston, focusing on changes in traveler preferences, occupancy rates, and revenue

Developed predictive model to forecast flight delays, clustered airports to enhance efficiency, and used Dijkstra’s algorithm for shortest flight paths, leading to fuel savings. Optimized intra-state connectivity with Kruskal's algorithm and created a linear programming model that minimized delay penalties.

K-means clustering and gradient boosting (XGBoost)

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

AIML Projects

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-label Classification Rules

A collection of boosting algorithms written in Rust 🦀

Kali Linux sanal makinesi kullanarak DDoS saldırılarının simülasyonunu gerçekleştirip, oluşturulan veri seti üzerinde makine öğrenme algoritmaları ile saldırı tespiti ve normal trafikten ayırma.

In my code portfolio, I generally try new techniques and methods in machine learning. I don't like only copying and pasting.

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

This project was completed as part of the CIT 650 "Intro To Big Data" course at Nile University.

Competing Risks and Survival Analysis