A curated list of gradient boosting research papers with implementations.

Tuning hyperparams fast with Hyperband

Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning

Ruby Scoring API for PMML

Demo on the capability of Yandex CatBoost gradient boosting classifier on a fictitious IBM HR dataset obtained from Kaggle. Data exploration, cleaning, preprocessing and model tuning are performed on the dataset

In this challenge we have given a directed social graph, and we have to predict missing links to recommend users (Link Prediction in graph)

Using various machine learning models to predict whether a company will go bankrupt

This contains the Jupyter Notebook and the Dataset for the mentioned Classification Predictive Modeling Project

Recognition of Persomnality Types from Facebook status using Machine Learning

Natural Language Processing for Multiclass Classification: A repository containing NLP techniques for multiclass classification of text data.

This project is our submission for the Kavach Hackathon 2023, in which we have created a browser extension that detects the links present in the email and classifies whether they are safe or not.

FederBoost's Federated Gradient Boosting Decision Tree Algorithm, Federated enabled Membership Inference

RocAuc Pairiwse objective for gradient boosting

Open source gradient boosting library

Analyze NASDAQ100 stock data. Used ARIMA + GARCH model and machine learning techniques Naive Bayes and Decision tree to determine if we go long or short for a given stock on a particular day

The aim is to find an optimal ML model (Decision Tree, Random Forest, Bagging or Boosting Classifiers with Hyper-parameter Tuning) to predict visa statuses for work visa applicants to US. This will help decrease the time spent processing applications (currently increasing at a rate of >9% annually) while formulating suitable profile of candidate…

A package to build Gradient boosted trees for ordinal labels

Data Science Challenge

A way to predict an NBA's players chance of making a shot using machine learning

Telecom Customer Churn Analysis & Prediction project uses Gradient Boosting for precise predictions, Power BI for churn pattern visualizations, and Streamlit for interactive insights. With robust code and meticulous data preprocessing, stakeholders access accurate predictions to optimize retention and drive profitability.