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.

Fit interpretable models. Explain blackbox machine learning.

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

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.

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

Conceptual basis of creating enemy learning behavior using gradient boosted regression models to make an effective counter choice.

Movie Revenue Prediction System predicts the revenue of a movie with 14 parameters: name, rating, genre, year, released, score, votes, director, writer, star, country, budget, company and runtime using gradient boosting______________________________ Training Accuracy: 91.58%____________ Testing Accuracy: 82.42%.

Predict whether a visitor to an e-commerce website will generate revenue by making a purchase during their session based on features such as the number of pages visited, session duration, type of visitor, operating system, browser, and other relevant attributes.

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.

Sales Forecasting

Impementaion of various AI models using Numpy