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3 cards from this section. Quick, good the night before a test.
What is data preprocessing?
Focus on data making it usable.
Why is data collection important?
Consider its role in starting the process.
Why is data quality important in IoT?
Think about the consequences of poor data.
True or False: Concept drift means your model needs updating over time.
Consider if the model can remain unchanged.
Explore how different techniques in data preprocessing can impact model accuracy. Provide a detailed explanation.
Consider both the benefits and drawbacks of improper – and proper – preprocessing.
Evaluate a real-world scenario where data quality significantly influenced the outcome of an IoT application.
Research case studies in IoT for examples.
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