rpartplot

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.

Determining what factors explain alcohol consumption among students.

Trabajo Práctico de la materia Laboratorio de Datos sobre un Modelo de Regresión para predecir viajes diarios en EcoBici y un Clasificador de noticias reales y falsas de un dataset

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

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.

Python port of R's rpart: recursive partitioning and regression trees, with a compiled C core (via cffi) and R-parity Python functions for fitting, pruning, predicting, and plotting trees. Supports classification, regression, Poisson/survival, and user-defined splits. GPL-2.0-or-later, derived from Therneau & Atkinson's rpart.

An AI-assisted, phase-by-phase conversion of R's rpart (CART decision trees) to a standalone Python package, r2py_rpart, reusing rpart's original C source via a fake R-API header layer and cffi, with a full technical report documenting the methodology.

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.

Random Forest, Decision tree and Logistic Regression