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.

Ce projet a été développé dans le contexte de la dissolution anticipée de l'Assemblée nationale française suivant les élections européennes de 2024. Le but est de prédire les résultats des élections législatives anticipées à partir des données des scrutins précédents, y compris les élections européennes, présidentielles et législatives précédentes

Generalized Random Forests

A machine learning method for the discovery of the minimum marker gene combinations for cell type identification from single-cell RNA sequencing

A project aimed to reduce dropout rate, consisted of building model and dashboard deployed on streamlit. User can input the data to predict if the student would dropout or not.

A comprehensive project to predict and analyze diabetes health data using advanced machine learning models, including Logistic Regression, Random Forest, and XGBoost. 📊🔍

The main goal of this project is to build a machine learning model that, given a Covid-19 patient's current symptom, status, and medical history, will predict whether the patient is in high risk or not.

Predict how effective employee performance is through actual productivity. [Deploy soon]

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.

Python library to forecast univariate time series through backtesting model selection

Files relevant for my bachelor thesis on different automatic emotion recognition approaches

Compendium of free ML reading resources

This project is created using Machine Learning and Regression methods- a statistical technique to predict the outcome of event which is to verify the users’ admission eligibility level, considering the universities they have chosen. This is achieved based on the algorithms implemented, when is user feed the application with the required information

"oxayavongsa/projects" is a public GitHub repository serving as a diverse AI/ML Project Portfolio. Using Python coding and Juptyer notebook for multiple methodologies to model statistical algorithms.

🍊 📊 💡 Orange: Interactive data analysis

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

Explore my Codsoft ML Internship tasks

Machine Learning inference engine for Microcontrollers and Embedded devices

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

ABC random forests for model choice and parameter estimation, pure C++ implementation