Automation framework for machine learning, forecasting, model evaluation, and interpretation.

Project in R for the exam of "Statistical Learning" for the Master's "Data Science for Economics" at UNIMI. It covers two separated projects: one for unsupervised learning (implementing PCA, K-means, Hierarchical Clustering...) and one for supervised learning (implementing Logistic, SVM, Random Forests...)

🎓 An unsupervised learning project analyzing PISA 2018 data for Finland. Uses PCA and K-Means in R to identify and profile distinct student clusters. Developed for the ULM course at ISCTE.

This repository stores the scigenex R library.

This app is intended to dynamically integrate machine learning techniques to explore multivariate data sets.

BBC news dataset pipeline : data collection, cleaning, and topic modelling with LDA/DTM

Tools for assessing clustering robustness

Analysis to identify patterns of U.S. colleges based on academic, financial, and demographic features

The multi-sample Gaussian mixture model (MSGMM) is a clustering model adapted to fitting multiple samples simultaneously using the EM algorithm.

Unsupervised learning project on atmospheric gas data using PCA and K-Means clustering

Individual Academic project for DSA1101: Introduction to Data Science

My forked version for course MTHM503 "Applications of Data Science and Statistics"

PCA-based clustering of student grades to explore academic performance patterns (R)

A machine learning project in R to forecast rainfall using multiple classification algorithms and performance comparison.

A tool for the multi-resolution characterization of molecular taxonomies in bulk and single-cell omics data

Script en R que aplica técnicas de Machine Learning no supervisado con el algoritmo Apriori para identificar patrones frecuentes y generar reglas de asociación a partir de transacciones contables categóricas.

Public teaching resource on unsupervised learning

Using Apache Spark for marketing analytics

In this repository you will find a project carried out in RStudio related to supervised and unsupervised statistics topics. In particular, the topics covered are: PCA, k-means, clustering, logistic regression, k-fold cross validation and decision trees

An R package to perform Spatial Fuzzy C-means