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Machine Learning馃 Predicci贸n de Atraso en Vuelos: Exploraci贸n de datos, modelos de clasificaci贸n (XGBoost, SVM), preparaci贸n de datos (One-Hot Encoding, PCA), y una interfaz de usuario simple en HTML + Flask. Herramientas utilizadas: Python, scikit-learn, XGBoost, Flask, y Postman.

Home Page: https://github.com/Kokit0/NW4daysChallenge_AtrasoVuelos

License: MIT License

Jupyter Notebook 98.64% Python 0.72% HTML 0.64%
api flask mlops mlops-workflow postman-api python rest-api

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