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.

Predicting Hepatocellular Carcinoma through Supervised Machine Learning

An R package to create and visualise fast-and-frugal decision trees (FFTs)

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.

This repository houses my solutions to a diverse range of machine learning assignments and projects completed during the ML Zoom Camp, a comprehensive 4-month machine learning boot camp.

Scripts from laboratories of course "Selected Topics of Machine Learning" from my 1st semester of Masters

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

adaXT: tree-based machine learning in Python

A collection of 8 Applied Data Science projects.

🌳 🎯 Cross Validated Decision Trees with Targeted Maximum Likelihood Estimation

Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn

AIML Projects

Reducing overfitting in perdiction in decision trees

Stakeholder-Specific Vulnerability Categorization

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

🍊 📊 💡 Orange: Interactive data analysis

All the Data Science projects I've done, during the course, or after.

This repository contains an implementation of decision tree and random forest algorithms from scratch in Python. Decision trees and random forests are popular machine learning algorithms used for classification and regression tasks. The goal of this project is to provide a clear and understandable implementation of these algorithms

This is the repository for Task-6 Completed as a DSBA Intern @ The Sparks Foundation

Python interface to automatically formulate Machine Learning models into Mixed-Integer Programs