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

This project detects whether a news is fake or not using machine learning.

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

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

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

A package to build Gradient boosted trees for ordinal labels

Pose estimation and prediction using Mediapipe and various ML models

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

This research goal is to build binary classifier model which are able to separate fraud transactions from non-fraud transactions.

Data Science Challenge

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

RocAuc Pairiwse objective for gradient boosting

Kaggle Machine Learning Competition Project : To classify activities into one of the six activities performed by individuals by reading the inertial sensors data collected using Smartphone.