xgboost-algorithm

Python code for common Machine Learning Algorithms

A curated list of gradient boosting research papers with implementations.

A fast xgboost feature selection algorithm

Extension of the awesome XGBoost to linear models at the leaves

Tuning XGBoost hyper-parameters with Simulated Annealing

All codes, both created and optimized for best results from the SuperDataScience Course

Modified XGBoost implementation from scratch with Numpy using Adam and RSMProp optimizers.

Career Guidance System Using Machine Learning Techniques

A lightweight gradient boosted decision tree package.

Perform a survival analysis based on the time-to-event (death event) for the subjects. Compare machine learning models to assess the likelihood of a death by heart failure condition. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases and heart failure condition.

Machine Learning Project using Kaggle dataset

A binary classification model is developed to predict the probability of paying back a loan by an applicant. Customer previous loan journey was used to extract useful features using different strategies such as manual and automated feature engineering, and deep learning (CNN, RNN). Various machine learning algorithms such as Boosted algorithms (XGBoost, LightGBM, CatBoost) and Deep Neural Network are used to develop a binary classifier and their performances were compared.

Determining the important factors that influences the customer or passenger satisfaction of an airlines using CRISP-DM methodology in Python and RapidMiner.

Data Science Python Beginner Level Project

Machine Learning Assignments of inuroun academy ML with master deployment and deep learning 29th Aug. 2020

Introduction to XGBoost with an Implementation in an iOS Application

The python notebook is on googles new collabatory tool. Its a churn model being run on 3 different algorithms to compare.

Machine-Learning: eXtreme Gradient-Boosting Algorithm Stress Testing

This repository contains various machine problems with solutions with various algorithms.

Machine learning Based Minor Project, which uses various classification Algorithms to classify the news into FAKE/REAL, on the basis of their Title and Body-Content. Data has been collected from 3 different sources and uses algorithms like Random Forest, SVM, Wordtovec and Logistic Regression. It gave 94% accuracy.