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35 lines
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35 lines
2.1 KiB
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"source_file": "3-Web-App/README.md",
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# Build a web app to use your ML model
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In this part of the curriculum, you'll explore a practical application of machine learning: how to save your Scikit-learn model as a file that can be used to make predictions in a web application. Once the model is saved, you'll learn how to integrate it into a web app built with Flask. You'll start by creating a model using data about UFO sightings! Then, you'll develop a web app that allows users to input a number of seconds along with latitude and longitude values to predict which country reported the UFO sighting.
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Photo by <a href="https://unsplash.com/@mdherren?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Michael Herren</a> on <a href="https://unsplash.com/s/photos/ufo?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
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## Lessons
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1. [Build a Web App](1-Web-App/README.md)
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## Credits
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"Build a Web App" was written with ♥️ by [Jen Looper](https://twitter.com/jenlooper).
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♥️ The quizzes were created by Rohan Raj.
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The dataset is provided by [Kaggle](https://www.kaggle.com/NUFORC/ufo-sightings).
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The web app architecture was partially inspired by [this article](https://towardsdatascience.com/how-to-easily-deploy-machine-learning-models-using-flask-b95af8fe34d4) and [this repo](https://github.com/abhinavsagar/machine-learning-deployment) by Abhinav Sagar.
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---
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**Disclaimer**:
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This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation. |