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5 cards from this section. Quick, good the night before a test.
What is data cleaning?
Think about why we need accurate data for analysis.
Why is normalization needed?
Consider how different units can affect calculations.
What is the primary goal of data cleaning?
Remember the importance of accurate data for reliable analysis.
True or False: Normalization is unnecessary if all data is already on a similar scale.
Think about data representation in machine learning models.
Given a dataset with missing values, describe two different methods you could use to address the issue, including the potential consequences of each approach.
Think about the trade-offs between data integrity and accuracy in predictions.
Imagine you have collected sensor data with significant outliers due to equipment malfunction. How would you identify and handle these outliers in your preprocessing steps?
Consider how outliers can skew the results in statistical analyses.
6 more questions in this section. Enroll free to practise them all.
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