Part of my PhD thesis - a system for reconstruction of damaged AIS data, consisting of 3 stages: clustering, anomaly detection and prediction

Repositorio donde exploro distintos algoritmos esenciales de machine learning en Python y R

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

Bayesian post-hoc regularization of Random Forests

Here we have fully implemented a number of algorithms related to machine learning

Beta Machine Learning Toolkit

Estimación de turbidez en el agua a la entrada de la planta de tratamiento de SAMEEP, utilizando los productos Sentinel-2 MSI L2A y aprendizaje automático.

BitPredictor - A cutting-edge machine learning-based solution for predicting cryptocurrency prices. Harnessing the power of advanced algorithms and data analysis techniques, this system aims to provide accurate and timely forecasts for Bitcoin and other cryptocurrencies.

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

Comparing random forest, support vector regression and xgboost in predicting housing prices.

Quantile Regression Forests compatible with scikit-learn.

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Annual mapping of soil organic carbon stock in Brazil 1985-present. Training field soil data

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

Scikit-learn compatible decision trees beyond those offered in scikit-learn

This project aims to enhance the accuracy and efficiency of stock market predictions by employing a sophisticated machine learning methodology. This project leverages the power of PySpark, a robust framework for distributed data processing, to handle large datasets and perform complex computations.

This project focuses on developing machine learning solutions for various use cases within 5G New Radio (NR) networks, specifically under the Open Radio Access Network (O-RAN) framework.

This project is dedicated to implementing various machine learning algorithms from scratch to gain a deeper understanding of how they work.

Utilising Machine Learning to predict wether a song on Spotify will be a hit or not