Building a Machine Learning Model for Water Quality Part 2

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In Part 2 of this tutorial, students continue learning how real-world machine learning can help solve environmental problems by turning their model into an interactive app. In Building a Machine Learning Model for Water Quality Part 2, we take the Jupyter Notebook code from Part 1 and transform it into a Streamlit app that predicts the water quality of lakes in New Zealand.

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This lesson is designed to be student-friendly and parent-approved, making complex AI concepts easier to understand through hands-on learning. It’s a great fit for schools, classrooms, and families interested in STEM education, environmental science, and beginner machine learning projects.✅

💡 Students will learn how machine learning models can be used beyond notebooks and applied in real-world applications—building confidence in coding, data science, and problem-solving skills.

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✔ GitHub repository with the Streamlit code:
https://github.com/technovation-curri...

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⏱️ 𝗖𝗵𝗮𝗽𝘁𝗲𝗿𝘀 (𝗧𝗶𝗺𝗲 𝗦𝘁𝗮𝗺𝗽𝘀) :
00:00 – Introduction and recap of Part 1
01:18 – Understanding the machine learning model output
03:05 – Introduction to Streamlit for beginners
05:12 – Setting up the Streamlit app structure
07:48 – Connecting the machine learning model to the app
10:21 – Displaying predictions in a student-friendly way
12:45 – Testing the water quality prediction app
14:30 – Final review and next steps

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