https://github.com/renatomaynard/supervised-machine-learning-models-pytorch-sklearn

This repository provides a comprehensive implementation of supervised machine learning models using PyTorch and Scikit-learn. It includes end-to-end workflows for both classification and regression tasks, covering data preprocessing, model training, evaluation, and comparison between traditional ML models
https://github.com/renatomaynard/supervised-machine-learning-models-pytorch-sklearn

applied-machine-learning classification data-preprocessing deep-learning feature-engineering machine-learning ml-models ml-pipeline model-comparison model-evaluation python pytorch regression scikit-learn supervised-learning-algorithms

Last synced: 10 months ago
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This repository provides a comprehensive implementation of supervised machine learning models using PyTorch and Scikit-learn. It includes end-to-end workflows for both classification and regression tasks, covering data preprocessing, model training, evaluation, and comparison between traditional ML models

• Host: GitHub
• URL: https://github.com/renatomaynard/supervised-machine-learning-models-pytorch-sklearn
• Owner: RenatoMaynard
• License: mit
• Created: 2025-04-02T18:54:01.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-04-02T18:56:26.000Z (10 months ago)
• Last Synced: 2025-04-02T19:42:14.698Z (10 months ago)
• Topics: applied-machine-learning, classification, data-preprocessing, deep-learning, feature-engineering, machine-learning, ml-models, ml-pipeline, model-comparison, model-evaluation, python, pytorch, regression, scikit-learn, supervised-learning-algorithms
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# Supervised Machine Learning Models using Pytorch and Scikit-Learn

This repository provides a comprehensive implementation of supervised machine learning models using PyTorch and Scikit-learn. It includes end-to-end workflows for both classification and regression tasks, covering data preprocessing, model training, evaluation, and comparison between traditional ML models