xgboost-algorithm

benedekrozemberczki/awesome-gradient-boosting-papers

A curated list of gradient boosting research papers with implementations.

chasedehan/BoostARoota

A fast xgboost feature selection algorithm

ldv1/LinXGBoost

Extension of the awesome XGBoost to linear models at the leaves

jinlow/forust

A lightweight gradient boosted decision tree package.

KSpiliop/Fraud_Detection

Tuning XGBoost hyper-parameters with Simulated Annealing

sauravmishra1710/Heart-Failure-Condition-And-Survival-Analysis

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.

farhanchoudhary/Machine_Learning_A-Z_All_Codes_and_Templates

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

ojasphansekar/Zillow-Home-Value-Prediction

XGBoost, LightGBM, LSTM, Linear Regression, Exploratory Data Analysis

vaishnavipatil29/Career-Guidance-ML-Project

Career Guidance System Using Machine Learning Techniques

jaydipkumar/Data-Science-Python

Data Science Python Beginner Level Project

RudreshVeerkhare/CustomXGBoost

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

yogeshwaran-shanmuganathan/Airline-Passenger-Satisfaction

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

pankajrawat9075/fantasy-sports-prediction

We have used our skill of machine learning along with our passion for cricket to predict the performance of players in the upcoming matches using ML Algorithms like random-forest and XG Boost

eswar3/Zillow-prediction-models

Machine Learning Project using Kaggle dataset

ali-ghorbani-k/Credit-Risk-Management

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.

Hashehri/Network-Traffic-Classification-UNSW-NB15

Binary Classification for detecting intrusion network attacks. In order, to emphasize how a network packet with certain features may have the potentials to become a serious threat to the network.

KrishArul26/Air-Quality-Index-prediction_with_deployment

India is one of the countries with the highest air pollution country. Generally, air pollution is assessed by PM value or air quality index value. For my further analysis, I have selected PM-2.5 value to determine the air quality prediction and the India-Bangalore region. Also, the data was collected through web scraping with the help of Beautiful Soup.

kush1912/FAKE-NEWS-DETECTION

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.

MitchellTesla/Max-Q

Machine-Learning: eXtreme Gradient-Boosting Algorithm Stress Testing

Mohsinrazaa/All-Machine-Learning-Algorithm

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

omarmhaimdat/xgboost_student_performance

Introduction to XGBoost with an Implementation in an iOS Application

sohammanjrekar/Multiple-Disease-Prediction-Webapp

Designed web app employs the Streamlit Python library for frontend design and communicates with backend ML models to predict the probability of diseases. It's capable of predicting whether someone has Diabetes, Heart issues, Parkinson's, Liver conditions, Hepatitis, Jaundice, and more based on the provided symptoms, medical history, and results.

ankit-kothari/Credit-Risk-Analysis

Predicting the ability of a borrower to pay back the loan through Traditional Machine Learning Models and comparing to Ensembling Methods

govardhan26/Parkinsons-Disease-Prediction

As an early diagnosis step machine learning classifiaction algorithms could be used in finding if the patient is prone to parkinsons disease.

IshtyM/Shipping-Pricing-Prediction

Predicting the supply chain shipment pricing based on the available factors in the dataset using the classical machine learning algorithms.

omerfarukeker/The-Complete-Journey

The Complete Journey Dataset: Churn Prediction

rajatbansal01/Machine_Learning-and-Data_Analysis

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

rishanki/Xgboost-VS-Catboost-Vs-LiteGBM

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

susanli2016/Machine-Learning-with-R

R codes for common Machine Learning Algorithms

leonardodepaula/xgbimputer

Extreme Gradient Boost imputer for Machine Learning.

TanerArslan/Benchmarking_Classifiers_after_SVM-RFE

Evaluating multiple classifiers after SVM-RFE (Support Vector Machine-Recursive Feature Elimination)

a3X3k/Spam-Email-Detection

datacrypto-analytics/crypto-analysis-cli

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

g-aditi/customer-personality-analysis

Using a Kaggle dataset, customer personality was analysed on the basis of their spending habits, income, education, and family size. K-Means, XGBoost, and SHAP Analysis were performed.

sakusuma/CreditCardFraudDetection

Although digital transactions in India registered a 51% growth in 2018-19, their safety remains a concern. Fraudulent activities have increased severalfold, with around 52,304 cases of credit/debit card fraud reported in FY'19 alone. Due to this steep increase in banking frauds, it is the need of the hour to detect these fraudulent transactions in time in order to help consumers as well as banks, who are losing their credit worth each day. Machine learning can play a vital role in detecting fraudulent transactions. Imagine you get a call from your bank, and the customer care executive informs you that your card is about to expire in a week. Immediately, you check your card details and realise that it will expire in the next 8 days. Now, in order to renew your membership, the executive asks you to verify a few details such as your credit card number, the expiry date and the CVV number. Will you share these details with the executive? In such situations, you need to be careful because the details that you might share with them could grant them unhindered access to your credit card account.The aim of this project is to predict fraudulent credit card transactions using machine learning models. The data set that you will be working on during this project was obtained from Kaggle. It contains thousands of individual transactions that took place over a course of two days and their respective labels.