This repository includes projects using datasets of structured data (non-Spark). The projects use Python, NumPy, Pandas, Matplotlib, Seaborn, TensorFlow, Pytorch, and Sklearn.

Benchmark for some usual automated machine learning, such as: AutoSklearn, MLJAR, H2O, TPOT and AutoGluon. All visualized via a Dash Web Application

Find out whether a product is Sportswear or not based on URL texts using Machine Learning in Python

Shows how to install auto-sklearn on an Azure Databricks cluster

AutoML Libraries for training multiple ML models in one go with less code.

An AutoML framework for classical machine learning algorithms, automating model selection and hyperparameter tuning through Bayesian optimization, portfolio-based meta-learning, and multi-fidelity evaluation using Successive Halving.

✉️ Generate personalized emails to professors effortlessly, leveraging AI to craft tailored messages based on your resume and their profiles.

Explainable Automated Machine Learning Framework for Predicting the Risk of Major Adverse Cardiac Event (MACE)

Warehouse Storage Optimization

KGpip - A Scalable AutoML Approach Based on Graph Neural Networks

TFG realizado en la Universidad de Burgos del desarrollo de una aplicación para el uso de un Radar de 60 GHz de la marca Acconeer.

In this repository we test AutoML approaches for time-series forecasting

Small tutorial on auto-sklearn which is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.

autosklearn-zeroconf is a fully automated binary classifier. It is based on the AutoML challenge winner auto-sklearn. Give it a dataset with known outcomes (labels) and it returns a list of predicted outcomes for your new data. It even estimates the precision for you! The engine is tuning massively parallel ensemble of machine learning pipelines…

Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.

Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.