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In a dataset containing a million samples where only a thousand lack data, what strategy can be employed to handle the missing records?

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What approach can be taken to handle missing values in a dataset with limited data?

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What is the first step in the feature engineering process?

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When is normalization typically applied in feature engineering?

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What is the purpose of feature selection in feature engineering?

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What is the correct sequence of steps in the feature engineering process?

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Which encoding technique expands the categorical feature into multiple binary columns, each representing a unique category?

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In One-Hot Encoding, how many columns are created for a categorical feature with n unique categories?

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Label Encoding involves assigning a unique numerical label to each category in a categorical feature. For example, converting categorical labels like "low," "medium," and "high" to numerical values 0, 1, and 2.

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One-Hot Encoding converts each categorical value into a new binary column and assigns a 1 or 0 to indicate the presence or absence of that category. For example, converting categorical values like "red," "blue," and "green" into three separate binary columns.

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