:evergreen_tree: broom helpers for decision tree methods (rpart, randomForest, and more!) :evergreen_tree:

Predict respiratory patient mortality in ICU units using the MIMIC III database

Web application using machine learning algorithms to predict whether an NBA team will cover the spread.

Use regression tree to predict firearm death rate with firearm law & CDC firearm death rate data.

Define, build, and evaluate machine learning models for real-world applications

Nations Flags Classification & Clustering project. :flags:

Analytics Vidya practice dataset for loan prediction

This project about the fitting a classification tree to the housing data sing R package rapart.

Decision tree classifier to predict the right classes of the observations in the 'Iris' data set.

Data analysis for Income-based on occupation

R & Python Projects

An implementation of of the CART-ANOVA algorithm for regression trees in Python

Team 3

Transferring Performance Prediction Models Across Different Hardware Platforms. Трансфер моделей прогнозирования производительности между различными аппаратными платформами

My approach and solution

No description provided.

This repository houses small projects (R and Python) exploring Machine Learning methods and algorithm description & implementation. Larger ML projects are housed in the Projects Repo.

Custom implementation of Random Forest based on rpart library.

an analysis was conducted on the Wine Data to discern the factors that play a pivotal role in determining the quality of wine. An exploration of the correlations between these attributes and the overall wine quality was undertaken, while considering the interplay with other relevant factors

This repository contains all the R code used in my dissertation project at the University of Manchester, 2025 (MATH6000S). Note that the regression tree code also contains the gradient boosting code, the EDA code contains the code for the plots seen in section 4.1, and the heights code is used in section 1.1.

Datamining en Economía y Finanzas - Churn Prediction - Trabajo Práctico 1

Classification And Regression Trees and Random Forest with applications to Big Data - course

Empirical Comparison of Regression Methods for Variability-Aware Performance Prediction. Эмпирическое сравние регрессионных методов для предсказания производительности конфигурируемых систем

MathSuccess - it is a dataset of 2600 rows containing student info and if they passed a highly competitive Math Class. I used 5 models to compare its accuracy in R-Studio. Models Used : ctree, rpart, svm, randomForest, nnet

Random forest and gradient boosting models applied to wearable accelerometer data classify bicep curl form into correct and common error categories.