Fast and Accurate ML in 3 Lines of Code
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Updated
May 24, 2024 - Python
Fast and Accurate ML in 3 Lines of Code
This project leverages AutoGluon in AWS SageMaker Studio to predict bike sharing demand, automating model training and tuning for accurate forecasting.
TSForecasting - Automated Time Series Forecasting Framework
Solution for Kaggle competition "Bike Sharing Demand". The solution using AutoGluon's Tabular Predictor provides a good overview of which model to choose as the base model for this problem.
Predicting Bike-sharing Demand Using Machine Learning with AutoGluon
This repository includes sample code for AutoML tools AutoGluon, AutoKeras, AutoSklearn, H2O, PyCaret, TPOT
This project focuses on using the AWS open-source AutoML library, AutoGluon, to predict bike sharing demand using the Kaggle Bike Sharing demand dataset.
Playing with autogluon and some cryptos data
Benchmark for some usual automated machine learning, such as: AutoSklearn, MLJAR, H2O, TPOT and AutoGluon. All visualized via a Dash Web Application
This repository includes the Colab notebooks used for the AutoML benchmarking study (object detection), and some of the FiftyOne scripts used to generate the datasets.
Classification-Techniques-For-Fraud-Detection
Automated & Augmented ML Toolbox for Image Classification
Corporate Credit Rating Prediction with AWS SageMaker JumpStart
El presente repositorio contiene el código que utilicé para realizar mi investigación de tesina: PREDICCIÓN DE LA INFLACIÓN MEXICANA EN TIEMPOS DE INCERTIDUMBRE: UNA EVALUACIÓN DE MODELOS DE APRENDIZAJE DE MÁQUINA Y SU IMPACTO EN LA PRECISIÓN DEL PRONÓSTICO (Erik Rosas, 2023).
In this project, our goal is to leverage Machine Learning Engineering techniques to participate in a Kaggle competition, utilizing the AutoGluon library.
AutoML with AutoGluon
Traffic analysis for Tor-based malware detection and classification
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