GitHub topics: gradient-boosting

Repositories

Widezaaaaaaaa/Gradient-Network

**Gradient Network** is a Layer-2 scaling platform on Testnet, allowing developers to build scalable, high-performance decentralized applications with optimized resource management.

Size: 1.95 KB - Last synced at: about 15 hours ago - Pushed at: about 15 hours ago - Stars: 0 - Forks: 0

EvanGks/random-forests-gradient-boosting

A comprehensive comparison and implementation of Random Forests and Gradient Boosting algorithms for supervised machine learning tasks, including code, analysis, and performance evaluation.

Language: Jupyter Notebook - Size: 134 KB - Last synced at: about 18 hours ago - Pushed at: about 19 hours ago - Stars: 1 - Forks: 0

mrapp-ke/MLRL-Boomer

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

Language: C++ - Size: 329 MB - Last synced at: 1 day ago - Pushed at: 1 day ago - Stars: 1 - Forks: 0

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Language: C++ - Size: 23.2 MB - Last synced at: about 24 hours ago - Pushed at: 1 day ago - Stars: 17,286 - Forks: 3,896

sushantdhruv2003/Weather-forecasting-with-machine-learning

A dataset of meteorological sensors with data cleaning and finding the best learning model for forecasting

Language: Jupyter Notebook - Size: 3.2 MB - Last synced at: 2 days ago - Pushed at: 2 days ago - Stars: 0 - Forks: 0

sibirbil/RuleDiscovery

Two algorithms based on linear programming to discover classification rules for interpretable learning.

Language: Python - Size: 89 MB - Last synced at: 4 days ago - Pushed at: 4 days ago - Stars: 23 - Forks: 2

mirecl/catboost-cgo

CatBoost a fast, scalable, high performance Gradient Boosting on Decision Trees library. Golang using Cgo for blazing fast inference CatBoost Model 🚀

Language: C - Size: 813 KB - Last synced at: 4 days ago - Pushed at: 4 days ago - Stars: 14 - Forks: 1

mlondschien/anchorboosting

Efficient tree-based nonlinear anchor regression and classification for Python

Language: Python - Size: 109 KB - Last synced at: 5 days ago - Pushed at: 5 days ago - Stars: 2 - Forks: 0

interpretml/interpret

Fit interpretable models. Explain blackbox machine learning.

Language: C++ - Size: 14.8 MB - Last synced at: 6 days ago - Pushed at: 6 days ago - Stars: 6,516 - Forks: 749

maazestic/Fire_Incident_Analysis

This repository contains a detailed analysis of global fire incidents using NASA's FIRMS satellite data. Explore machine learning techniques for data cleaning, classification, and anomaly detection to uncover insights about fire activity. 🔥👨💻

Language: Jupyter Notebook - Size: 15.2 MB - Last synced at: 6 days ago - Pushed at: 6 days ago - Stars: 0 - Forks: 0

catboost/catboost

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Language: C++ - Size: 1.67 GB - Last synced at: 7 days ago - Pushed at: 7 days ago - Stars: 8,417 - Forks: 1,227

Vaishnavi-vi/Household_Energy_Usage_Forecast_--

The Household Power Consumption Dataset contains measurements of electric power usage in a single household in France, collected over a period of 4 years at one-minute intervals.

Language: Jupyter Notebook - Size: 21.4 MB - Last synced at: 7 days ago - Pushed at: 7 days ago - Stars: 1 - Forks: 0

shap/shap

A game theoretic approach to explain the output of any machine learning model.

Language: Jupyter Notebook - Size: 282 MB - Last synced at: 7 days ago - Pushed at: 8 days ago - Stars: 23,949 - Forks: 3,369

google/yggdrasil-decision-forests

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

Language: C++ - Size: 41.1 MB - Last synced at: 8 days ago - Pushed at: 8 days ago - Stars: 585 - Forks: 61

igopalakrishna/colorectal-cancer-prediction

Full-stack MLOps pipeline for predicting colorectal cancer patient survival using Gradient Boosting, Kubeflow Pipelines, MLflow, and Flask. Designed for hospitals, researchers, and real-world healthcare applications.

Language: Jupyter Notebook - Size: 6.58 MB - Last synced at: 9 days ago - Pushed at: 9 days ago - Stars: 0 - Forks: 0

Rodwanbagdadi/Fake_News_Detection

This is my current Graduation Project for Fake News Detection

Language: HTML - Size: 20.4 MB - Last synced at: 10 days ago - Pushed at: 10 days ago - Stars: 0 - Forks: 0

siboehm/lleaves

Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.

Language: Python - Size: 4.75 MB - Last synced at: 6 days ago - Pushed at: about 1 month ago - Stars: 420 - Forks: 32

Chetnas8/Fire_Incident_Analysis

Comprehensive ML analysis of global fire incidents using NASA FIRMS satellite data. Includes data cleaning, EDA, classification, regression, anomaly detection, and visuals. Highlights feature importance and key insights into fire confidence and intensity across different regions.

Language: Jupyter Notebook - Size: 15.2 MB - Last synced at: 11 days ago - Pushed at: 11 days ago - Stars: 0 - Forks: 0

EpistasisLab/tpot

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Language: Jupyter Notebook - Size: 87.2 MB - Last synced at: 8 days ago - Pushed at: 26 days ago - Stars: 9,908 - Forks: 1,578

lahirudiz/rdash

Interactive data-visualisation dashboard that predicts student performance with K-Nearest Neighbors (KNN), Support Vector Machines, and Gradient Boosting models.

Language: R - Size: 24.4 KB - Last synced at: 12 days ago - Pushed at: 12 days ago - Stars: 0 - Forks: 0

abdesemt/GBoost

A tool for Boosting Discord Servers

Size: 8.79 KB - Last synced at: 12 days ago - Pushed at: 12 days ago - Stars: 0 - Forks: 0

elucideye/drishti

Real time eye tracking for embedded and mobile devices.

Language: C++ - Size: 12.2 MB - Last synced at: 8 days ago - Pushed at: almost 6 years ago - Stars: 397 - Forks: 82

x4nth055/emotion-recognition-using-speech

Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras

Language: Python - Size: 944 MB - Last synced at: 10 days ago - Pushed at: over 1 year ago - Stars: 633 - Forks: 243

perpetual-ml/perpetual

A self-generalizing gradient boosting machine that doesn't need hyperparameter optimization

Language: Rust - Size: 1.43 MB - Last synced at: 12 days ago - Pushed at: 12 days ago - Stars: 498 - Forks: 26

ClimbsRocks/auto_ml

[UNMAINTAINED] Automated machine learning for analytics & production

Language: Python - Size: 1.38 MB - Last synced at: 13 days ago - Pushed at: over 4 years ago - Stars: 1,649 - Forks: 312

tensorflow/decision-forests

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

Language: Python - Size: 5.94 MB - Last synced at: 5 days ago - Pushed at: 22 days ago - Stars: 680 - Forks: 113

TorchEnsemble-Community/Ensemble-Pytorch

A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

Language: Python - Size: 1.56 MB - Last synced at: 13 days ago - Pushed at: 12 months ago - Stars: 1,021 - Forks: 94

RektPunk/MQBoost

Multiple quantiles estimation model maintaining non-crossing condition (or monotone quantile condition) using LightGBM and XGBoost

Language: Python - Size: 752 KB - Last synced at: 7 days ago - Pushed at: 7 months ago - Stars: 32 - Forks: 6

stanfordmlgroup/ngboost

Natural Gradient Boosting for Probabilistic Prediction

Language: Python - Size: 12 MB - Last synced at: 18 days ago - Pushed at: 18 days ago - Stars: 1,711 - Forks: 233

blei-lab/treeffuser

Treeffuser is an easy-to-use package for probabilistic prediction and probabilistic regression on tabular data with tree-based diffusion models.

Language: Jupyter Notebook - Size: 80.3 MB - Last synced at: 19 days ago - Pushed at: 19 days ago - Stars: 44 - Forks: 4

EpistasisLab/tpot2

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Language: Jupyter Notebook - Size: 10.5 MB - Last synced at: 17 days ago - Pushed at: 4 months ago - Stars: 232 - Forks: 31

ds-wook/learning-to-rank

LTR with gradient boosting

Language: Python - Size: 248 KB - Last synced at: 20 days ago - Pushed at: 20 days ago - Stars: 7 - Forks: 0

soda-inria/hazardous

Competing Risks and Survival Analysis

Language: Python - Size: 9.22 MB - Last synced at: 18 days ago - Pushed at: 21 days ago - Stars: 101 - Forks: 18

benedekrozemberczki/awesome-decision-tree-papers

A collection of research papers on decision, classification and regression trees with implementations.

Language: Python - Size: 870 KB - Last synced at: 13 days ago - Pushed at: about 1 year ago - Stars: 2,400 - Forks: 342

levisstrauss/Bike-Rental-Forecasting-System-with-AutoGluon

Machine learning project achieving 66% RMSE improvement in bike sharing demand prediction using AutoGluon, temporal feature engineering, and systematic hyperparameter optimization. Demonstrates iterative model development from baseline (1.32 RMSE) to optimized solution (0.45 RMSE).

Language: HTML - Size: 4.49 MB - Last synced at: 21 days ago - Pushed at: 21 days ago - Stars: 0 - Forks: 0

sankoktas/bhi360-fall-detection

Fall detection system using Bosch BHI360 sensor data with time-series labeling, feature extraction, and machine learning (LOSO CV + Gradient Boosting).

Language: Jupyter Notebook - Size: 49.5 MB - Last synced at: 21 days ago - Pushed at: 21 days ago - Stars: 0 - Forks: 0

sberbank-ai-lab/LightAutoML 📦

LAMA - automatic model creation framework

Language: Python - Size: 23.4 MB - Last synced at: 9 days ago - Pushed at: about 3 years ago - Stars: 925 - Forks: 96

bmmunga/abc-customer_engagement_ml

Machine-learning predictive model to analyse customer data, predict engagement likelihood, and surface actionable insights.

Language: Jupyter Notebook - Size: 2.27 MB - Last synced at: 14 days ago - Pushed at: 22 days ago - Stars: 1 - Forks: 0

ottenbreit-data-science/aplr

APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.

Language: C++ - Size: 6.13 MB - Last synced at: 18 days ago - Pushed at: about 2 months ago - Stars: 19 - Forks: 5

xRiskLab/xBooster

Explainable Boosted Scoring with Python: turning XGBoost and CatBoost classifiers into explainable scorecards

Language: Python - Size: 7.52 MB - Last synced at: 12 days ago - Pushed at: about 2 months ago - Stars: 15 - Forks: 4

ddbourgin/numpy-ml

Machine learning, in numpy

Language: Python - Size: 10 MB - Last synced at: 23 days ago - Pushed at: over 1 year ago - Stars: 16,097 - Forks: 3,803

Evovest/EvoTrees.jl

Boosted trees in Julia

Language: Julia - Size: 50.8 MB - Last synced at: 19 days ago - Pushed at: 22 days ago - Stars: 187 - Forks: 22

Cainder098/fraud-detection

Predicting transaction fraud using classification problems such as Guardian Boosting as well as user interfaces using Streamlite

Language: Jupyter Notebook - Size: 5.01 MB - Last synced at: 24 days ago - Pushed at: 24 days ago - Stars: 0 - Forks: 0

benedekrozemberczki/awesome-fraud-detection-papers

A curated list of data mining papers about fraud detection.

Language: Python - Size: 490 KB - Last synced at: 19 days ago - Pushed at: about 1 year ago - Stars: 1,699 - Forks: 313

abhinabasaha/ml

Language: Jupyter Notebook - Size: 960 KB - Last synced at: 24 days ago - Pushed at: 24 days ago - Stars: 2 - Forks: 0

PaoloGiordani/HybridTreeBoosting.jl

Hytrid Tree Boosting

Language: Julia - Size: 22 MB - Last synced at: about 21 hours ago - Pushed at: 28 days ago - Stars: 1 - Forks: 0

serengil/chefboost

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

Language: Python - Size: 1.09 MB - Last synced at: 13 days ago - Pushed at: 2 months ago - Stars: 476 - Forks: 101

Agoons20/Portfolio-Mgmt-And-Machine-Learning-in-Finance

Investment Analysis and Asset Mgmt, Time Series Analysis & Forecasting, Machine Learning in Finance & Causal Inference Methods

Language: Jupyter Notebook - Size: 3.83 MB - Last synced at: 28 days ago - Pushed at: 28 days ago - Stars: 0 - Forks: 0

Yahlawat/Credit-Card-Default-Prediction

Predicting default payments using gradient boosting and ensemble machine learning models. Includes EDA, model comparison (Random Forest, XGBoost, LightGBM, etc.), ROC analysis, and feature importance on Taiwanese credit card client data.

Language: Jupyter Notebook - Size: 3.25 MB - Last synced at: 28 days ago - Pushed at: 28 days ago - Stars: 0 - Forks: 0

ggeop/Kaggle-Timeseries-Forecasting

😺 CatBoost Model Per Family

Language: Jupyter Notebook - Size: 72.3 KB - Last synced at: 13 days ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 2

cosmoimd/feature-selection-gates

Feature Selection Gates with Gradient Routing

Language: Python - Size: 25.5 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 4 - Forks: 0

sorna-fast/Weather-forecasting-with-machine-learning

A dataset of meteorological sensors with data cleaning and finding the best learning model for forecasting

Language: Jupyter Notebook - Size: 5.4 MB - Last synced at: 8 days ago - Pushed at: about 1 month ago - Stars: 1 - Forks: 0

samsonq/snapboost

Heterogeneous Newton Boosting Machine using decision trees and kernel ridges as learners.

Language: Python - Size: 5.86 KB - Last synced at: 18 days ago - Pushed at: over 3 years ago - Stars: 5 - Forks: 1

CrSamson/Predicting-Heart-Disease

Modèles de machine learning pour prédire les maladies cardiaques à partir de données cliniques. Comparaison de K-NN, arbres de décision, Random Forest, Gradient Boosting, XGBoost et réseaux de neurones.

Language: Jupyter Notebook - Size: 0 Bytes - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

dimitris-markopoulos/trees-ensembles-neural-networks

Machine learning models including decision trees, random forests, adaboost, gradient boosting, and neural networks applied to structured data for classification tasks.

Language: Jupyter Notebook - Size: 27.9 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

DavidMembreno/Data-Science

This repository contains essential project files for various data science and data analysis projects.

Language: Jupyter Notebook - Size: 9.14 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

Vikranth3140/Movie-Revenue-Prediction

Movie Revenue Prediction System predicts the revenue of a movie with 14 parameters: name, rating, genre, year, released, score, votes, director, writer, star, country, budget, company and runtime using gradient boosting______________________________ Training Accuracy: 91.58%____________ Testing Accuracy: 82.42%.

Language: TeX - Size: 27.9 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 12 - Forks: 5

sergio-sanz-rodriguez/Skin-Cancer-Detection

Detection of skin cancer cases using image-based ML and DL models.

Language: Jupyter Notebook - Size: 94.5 MB - Last synced at: 23 days ago - Pushed at: 6 months ago - Stars: 1 - Forks: 1

shashikathi/Wine-Quality-Prediction-

This project aims to predict wine quality based on physicochemical properties. Using machine learning models including Random Forest and Gradient Boosting Classifier, this analysis identifies key factors affecting wine quality and builds a predictive model to classify wines.

Language: Jupyter Notebook - Size: 1.66 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 1 - Forks: 0

benedekrozemberczki/tigerlily

TigerLily: Finding drug interactions in silico with the Graph.

Language: Jupyter Notebook - Size: 14.3 MB - Last synced at: 30 days ago - Pushed at: over 2 years ago - Stars: 100 - Forks: 9

Yahlawat/Spotify-Popularity-Prediction

ML project predicting Spotify song popularity using audio features, metadata, and gradient boosting models (XGBoost, LightGBM, CatBoost).

Language: Jupyter Notebook - Size: 1.09 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

heather-253/house-prices-prediction

Predict house prices using feature engineering, Random Forest, and Gradient Boosting models. Includes full machine learning workflow: EDA, preprocessing, modeling, and submission file creation.

Language: Jupyter Notebook - Size: 2.93 KB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

jd-opensource/UTBoost

A powerful tree-based uplift modeling system.

Language: C++ - Size: 188 KB - Last synced at: 23 days ago - Pushed at: over 1 year ago - Stars: 31 - Forks: 2

krishnaura45/loan-approval-predictor

🧪Predicting Loan Approvals 🚀Hill Climbing 🧮Ensemble Techniques

Language: Jupyter Notebook - Size: 692 KB - Last synced at: 24 days ago - Pushed at: about 2 months ago - Stars: 2 - Forks: 0

Wb-az/ML-airbnb-paris-analytics-and-price-prediction

Airbnb Paris - analytics and accommodation price prediction

Language: Jupyter Notebook - Size: 36.8 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

SHAIK-AFSANA/diabetespredictor

Built using Machine Learning models and deployed via MySQL and Streamlite, this diabetes predictor offers a comprehensive web platform for user authentication, data storage, and interactive access to diabetes-related information. This diabetes predictor aims to enhance early intervention, reducing diabetes-related complications.

Language: Jupyter Notebook - Size: 8.16 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

closest-git/LiteMORT

A memory efficient GBDT on adaptive distributions. Much faster than LightGBM with higher accuracy. Implicit merge operation.

Language: C++ - Size: 1.47 MB - Last synced at: 1 day ago - Pushed at: over 5 years ago - Stars: 58 - Forks: 9

feedzai/fairgbm

Train Gradient Boosting models that are both high-performance *and* Fair!

Language: C++ - Size: 43 MB - Last synced at: 16 days ago - Pushed at: 12 months ago - Stars: 104 - Forks: 6

rmitsuboshi/miniboosts

A collection of boosting algorithms written in Rust 🦀

Language: Rust - Size: 4.12 MB - Last synced at: 27 days ago - Pushed at: 4 months ago - Stars: 53 - Forks: 5

MahtabRanjbar/Customer_Churn_Prediction

Full‑stack Customer Churn Prediction app – FastAPI backend, Streamlit dashboard, PostgreSQL & Docker

Language: Python - Size: 5.18 MB - Last synced at: about 1 month ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

benedekrozemberczki/awesome-gradient-boosting-papers

A curated list of gradient boosting research papers with implementations.

Language: Python - Size: 1.48 MB - Last synced at: 18 days ago - Pushed at: about 1 year ago - Stars: 1,022 - Forks: 160

john-fante/john-fante

In my code portfolio, I generally try new techniques and methods in machine learning. I don't like only copying and pasting.

Size: 318 KB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

lebedevaale/early_warning_model

Model for the prediction of the critical transitions in the volume of trading in the financial hourly data

Language: Jupyter Notebook - Size: 842 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

sergio11/diabetes_prediction_ml

Predicting diabetes using machine learning models based on medical data 📊💉. The goal is to create an accurate and reliable diagnostic tool for early detection 🏥🤖.

Language: Jupyter Notebook - Size: 13.6 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 2 - Forks: 2

ankush-003/godt

A high-performance implementation of tree-based machine learning algorithms in Go

Language: Go - Size: 16.6 KB - Last synced at: about 1 month ago - Pushed at: about 2 months ago - Stars: 1 - Forks: 0

rishiraj/autolgbm

LightGBM + Optuna: Auto train LightGBM directly from CSV files, Auto tune them using Optuna, Auto serve best model using FastAPI. Inspired by Abhishek Thakur's AutoXGB.

Language: Python - Size: 1.06 MB - Last synced at: 10 days ago - Pushed at: over 3 years ago - Stars: 37 - Forks: 5

YangLabHKUST/mfair

mfair: Matrix Factorization with Auxiliary Information in R

Language: R - Size: 41.6 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 8 - Forks: 1

saifalibaig/Credit_Risk_Analysis

This project analyzes the "Give Me Some Credit" dataset from Kaggle to build predictive models that can identify borrowers who are likely to default on their loans. The system helps financial institutions make more informed lending decisions and improve their credit risk management strategies.

Language: Jupyter Notebook - Size: 2.78 MB - Last synced at: 25 days ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

somjit101/Facebook-Friend-Recommendation

This is a friend recommendation systems which are used on social media platforms (e.g. Facebook, Instagram, Twitter) to suggest friends/new connections based on common interests, workplace, common friends etc. using Graph Mining techniques. Here, we are given a social graph, i.e. a graph structure where nodes are individuals on social media platforms and a directed edges (or 'links') indicates that one person 'follows' the other, or are 'friends' on social media. Now, the task is to predict newer edges to be offered as 'friend suggestions'.

Language: Jupyter Notebook - Size: 770 KB - Last synced at: about 2 months ago - Pushed at: over 1 year ago - Stars: 10 - Forks: 3

MoinDalvs/Gradient_Boosting_Algorithms_From_Scratch

4 Boosting Algorithms You Should Know – GBM, XGBoost, LightGBM & CatBoost

Language: Jupyter Notebook - Size: 1.08 MB - Last synced at: about 2 months ago - Pushed at: almost 3 years ago - Stars: 9 - Forks: 0

ArunabhaPani/kaggle_house_price_prediction_advanced_regression_ml_model

started by analysing and determining the aspects required for tuning of the final ml model. Performed Eda and feature engineering inorder to determine the import parameters of the dataset and to derive more useful features, finally creating a ml model by using various different basic and advanced regression techniques.

Language: Jupyter Notebook - Size: 1.69 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

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.

Language: Jupyter Notebook - Size: 10.2 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 3 - Forks: 0

skumbhar272002/heart-disease-classification

Heart disease classification using machine learning algorithms with hyperparameter tuning for optimized model performance. Algorithms include XGBoost, Random Forest, Logistic Regression, and moreto find the best model for accurate heart disease prediction.

Language: Python - Size: 3.05 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 1 - Forks: 0

chaitjo/working-women

Code for the paper 'Working Women and Caste in India' (ICLR 2019 AI for Social Good Workshop)

Language: Jupyter Notebook - Size: 212 MB - Last synced at: 8 days ago - Pushed at: 12 months ago - Stars: 16 - Forks: 3

MMBazel/Classifying-Sales-Calls

Turning salesforce lead, oppty, & sales activities data => Sales predictions using pandas, Scikit-learn, SQLAlchemy, Redshift, XGBoost Classifier

Language: Jupyter Notebook - Size: 6.95 MB - Last synced at: 29 days ago - Pushed at: over 4 years ago - Stars: 27 - Forks: 10

Timur-Maistermind/Machine-Learning-Roadmap

Machine Learning Roadmap for 2025. Step-by-step guide to become a Data Scientist. Covers the best free learning resources from Python basics to Deep Learning and MLOps.

Size: 3.72 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 73 - Forks: 5

mgsandaruwan/Diabetic_Risk_Prediction_App

A Flask web application that predicts diabetes risk using machine learning. The model is trained on the Pima Indians Diabetes Dataset and optimized to prioritize false positives over false negatives for medical safety. Features include patient data input, real-time predictions, and detailed risk assessment.

Language: Jupyter Notebook - Size: 2.65 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

RishiMdvrm/Comprehensive-analysis-of-EPL-match-data

This project analyzes EPL matches using machine learning and statistical methods, focusing on factors like team formations and home advantage to predict match outcomes and provide actionable insights.

Language: Jupyter Notebook - Size: 6.86 MB - Last synced at: about 1 month ago - Pushed at: 6 months ago - Stars: 0 - Forks: 1

afersantos/ejercicios-machine-learning

Proyecto académico de Machine Learning realizado con Python

Language: Jupyter Notebook - Size: 0 Bytes - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

albiagro/students-grade-prediction

This project focuses on data analysis and grade prediction for students enrolled in a Portuguese language course. Using the Student Alcohol Consumption dataset, we aim to understand how personal, social, and academic features influence students' final grades.

Language: Jupyter Notebook - Size: 605 KB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0