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.

Data analysis, clustering algorithms and forecasting for the demand of medicines in french hospitals problem.

Common machine learning algorithm implementations

Accelerated Oblique Random Survival Forests

Predicting the amount of streams a song will get based on various features given through the data.

An application that allow the user to log in (and access to all his data), and connect to external distributors, in order to get the coffee generated by a Machine Learning algorithm

Final project for AC209A: Data Science 1 @ Harvard University. This project aims to understand how different factors affect whether a person develops dementia by utilizing various ML models to predict diagnosis outcomes.

Predicting bank churn rates with machine learning models (decision trees, random forest, & xgboost)

This repository contains the LifeExpectancy Prediction Project, a comprehensive data science project aimed at predicting life expectancy based on various health, economic, and social factors. The project includes steps for data preprocessing, exploratory data analysis (EDA), model selection, training, hyperparameter tuning, and model interpretation

Streamlit app for predicting sentiment in Spotify reviews in Indonesian and English.

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

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.

A curated list of Best Artificial Intelligence Resources

📈 Bitcoin Price Prediction using Random Forest Regressor 🧠

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

Machine Learning para analisis de Encuestas de Hogares. Modelos de Random Forest para predecir caracteristicas de hogares argentinos usando EPH y datos del Censo.

Predicting University Admission Chances, where we explore the likelihood of admission for prospective students based on various factors. Leveraging machine learning, we have employed two powerful algorithms, Decision Tree and Random Forest, to predict the chances of admission.

Built a deep learning-based model to recommend movies based on user sentiment. Extracted data using Twitter API, preprocessed data using NLTK, and built machine learning models using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classification methods. Deployed the model on Airflow/EC2 and stored results in Amazon S3. Achieved 70%

Let ML steer a miniature car for you. :O

This repository contains a detailed analysis of the Spambase Dataset using different classification algorithms, including Logistic Regression, Logistic Regression with Backward Feature Elimination (BFE), Support Vector Machine (SVM), SVM with Normalized Data, Decision Trees, Random Forest, K-Nearest Neighbors (K-NN), and K-NN with Normalized Data.