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.

Machine Learning Project using Kaggle dataset

Career Guidance System Using Machine Learning Techniques

Create a model to assess the likelihood of a death by heart failure event. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases.

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

Machine-Learning: eXtreme Gradient-Boosting Algorithm Stress Testing

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

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.

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.

Data Science Python Beginner Level Project

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

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.

Analise todas as criptomoedas disponíveis na binance spot com algoritmos Machine Learning.

R codes for common Machine Learning Algorithms

The problem that this case study is dealing with predicts the location that a user is most likely to book for the first time. The accurate prediction helps to decrease the average time required to book by sharing more personalized recommendations and also in better forecasting of the demand. We use the browser’s session data as well as the user’s demographic information that is provided to us to create features that help in solving the problem.

Machine Learning Project on Imbalanced Data in R

Metis Project 3: Supervised Machine Learning with a Categorical Target (predicting credit card fraud)

The Complete Journey Dataset: Churn Prediction

In this project, I have predicted Housing sales price prices for King County,USA which includes Seattle. It includes homes sold between May 2014 and May 2015. It has 19 house features plus the price and the id columns, along with 21613 observations. In this project I have done the implementation of different Boosting regression machine learning models such as Gradient Boosting, eXtreme Gradient Boosting (XGB) and Adaboost. In this project, I have also used Permutation Importance for filtering the irrelevant features of the dataset. In this project, I have predicted Housing sales price prices for King County,USA which includes Seattle. It includes homes sold between May 2014 and May 2015. It has 19 house features plus the price and the id columns, along with 21613 observations. In this project I have done the implementation of different Boosting regression machine learning models such as Gradient Boosting, eXtreme Gradient Boosting (XGB) and Adaboost. In this project, I have also used Permutation Importance for filtering the irrelevant features of the dataset. Maximum Accuracy achieved around 98.59%.

Machine learning tutorial with examples

Extreme Gradient Boosting (XGBoost) with R and H2o for Stroke Prediction

Club Mahindra Data Olympics

The objective of this project is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.