gradient-boosting-classifier
There are 264 repositories under gradient-boosting-classifier topic.
benedekrozemberczki/awesome-gradient-boosting-papers
A curated list of gradient boosting research papers with implementations.
zygmuntz/hyperband
Tuning hyperparams fast with Hyperband
sharmapratik88/AIML-Projects
Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning
kapilsinghnegi/Fake-News-Detection
This project detects whether a news is fake or not using machine learning.
asafschers/scoruby
Ruby Scoring API for PMML
KwokHing/YandexCatBoost-Python-Demo
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
Snigdho8869/Multiclass-Text-Classification
Natural Language Processing for Multiclass Classification: A repository containing NLP techniques for multiclass classification of text data.
wangy8989/Bankruptcy-Prediction-using-Machine-Learning
Using various machine learning models to predict whether a company will go bankrupt
krpiyush5/Facebook-Friend-Recommendation-using-Graph-Mining
In this challenge we have given a directed social graph, and we have to predict missing links to recommend users (Link Prediction in graph)
indrapaul824/Coronary-Heart-Disease-Prediction
This contains the Jupyter Notebook and the Dataset for the mentioned Classification Predictive Modeling Project
adamingas/ordinalgbt
A package to build Gradient boosted trees for ordinal labels
dayfundora/Personality-Type
Recognition of Persomnality Types from Facebook status using Machine Learning
Jaap-Meerhof/Federated_XGBoost_Python
FederBoost's Federated Gradient Boosting Decision Tree Algorithm, Federated enabled Membership Inference
ArnabKumarRoy02/Phishing-attack-detection
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.
DandiMahendris/Auto-Insurance-Fraud-Detection
This research goal is to build binary classifier model which are able to separate fraud transactions from non-fraud transactions.
zin288/DanceVision-AI-Driven-Dance-Proficiency-Assessment
Pose estimation and prediction using Mediapipe and various ML models
abdullahsaka/Capital_One-Data_Challenge
Data Science Challenge
DmitryAsdre/rocauc_pairwise
RocAuc Pairiwse objective for gradient boosting
shraddhasomani/Statistical-Modeling-for-NASDAQ100-Stock
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
leffff/stackboost
Open source gradient boosting library
rochitasundar/Customer-profiling-using-ML-EasyVisa
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 candidates more likely to have the visa certified.
sushant1827/Human-Activity-Recognition-with-Smartphones
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.
VibolvatanakPOCH/Telecom-Customer-Churn-Analysis-Prediction
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.
AtharvaKulkarniIT/Fake-News-Detection-using-Machine-Learning
This repository hosts a Jupyter notebook for Fake News Detection, utilizing machine learning algorithms like Logistic Regression , Gradient Boosting Classifier , Random Forest and Decision Tree. The project covers data preprocessing, analysis and manual testing of news articles, with added multi language support using Google Translate API .
therealAJ/buckets-plus-plus
A way to predict an NBA's players chance of making a shot using machine learning
brian-kipkoech-tanui/binaryclassification
Classification
luuisotorres/Credit-Card-Fraud-Detection
For this project, I used four different classification algorithms to detect fraudulent credit card transactions.
mborhi/IPO-Underpricing
Predicting short-term IPO returns. Course work for CSCI-B351 at Indiana University.
tboudart/Life-Expectancy-Regression-Analysis-and-Classification
I contributed to a group project using the Life Expectancy (WHO) dataset from Kaggle where I performed regression analysis to predict life expectancy and classification to classify countries as developed or developing. The project was completed in Python using the pandas, Matplotlib, NumPy, seaborn, scikit-learn, and statsmodels libraries. The regression models were fitted on the entire dataset, along with subsets for developed and developing countries. I tested ordinary least squares, lasso, ridge, and random forest regression models. Random forest regression performed the best on all three datasets and did not overfit the training set. The testing set R2 was .96 for the entire dataset and developing country subset. The developed country subset achieved an R2 of .8. I tested seven different classification algorithms to classify a country as developing or developed. The models obtained testing set balanced accuracies ranging from 86% - 99%. From best to worst, the models included gradient boosting, random forest, Adaptive Boosting (AdaBoost), decision tree, k-nearest neighbors, support-vector machines, and naive Bayes. I tuned all the models' hyperparameters. None of the models overfitted the training set.
Agnij-Moitra/MSBoost
MSBoost is a gradient boosting algorithm that improves performance by selecting the best model from multiple parallel-trained models for each layer, excelling in small and noisy datasets.
billy-enrizky/Loan-Prediction-Status
Explore an ML model with Logistic Regression, SVM, Gradient Boosting, Random Forest, and Decision Tree, enhanced via Hyperparameter Tuning. Experience our GUI-based ML model with 82.49% accuracy. Try it now!
HCYENDLURI/Comparing-6-Classifiers-for-Sepsis-Dataset
To Detect Sepsis Disease using six Classifiers on clinical data
PavanParchuri/Body-Fitness-Prediction
⚡ BODY FITNESS PREDICTION is a Machine Learning based web application that is used to predict the fitness levels of a person (Active/Inactive).
Sachinthotre/Credit_Card_Fraud_Detection
Utilizing machine learning models including logistic regression, random forest, gradient boosting, and neural networks to identify fraudulent credit card transactions. Dataset, consisting of PCA-transformed features and unbalanced classes, required precision-recall metrics for accurate evaluation. Developed in Python using TensorFlow and scikit.
sushant1827/Machine-Learning-for-Predictive-Maintenance
Demonstrate the application of machine learning on a real-world predictive maintenance dataset, using measurements from actual industrial equipment.
VivekSagarSingh/Probability-of-Credit-card-Default
Classification problem using multiple ML Algorithms