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    Question

    Which of the following is the most appropriate method to

    handle missing data in a dataset for predictive modeling?
    A Deleting all rows with missing values. Correct Answer Incorrect Answer
    B Replacing missing values with the column mean, median, or mode. Correct Answer Incorrect Answer
    C Leaving the missing data as is and proceeding with analysis. Correct Answer Incorrect Answer
    D Filling missing values with arbitrary constants like zero. Correct Answer Incorrect Answer
    E Duplicating rows with missing values to retain data balance. Correct Answer Incorrect Answer

    Solution

    Explanation: Replacing missing data with statistical measures like mean (for continuous data), median (for skewed distributions), or mode (for categorical data) is a robust imputation technique. This approach minimizes the loss of data while maintaining the dataset's integrity. It is particularly effective when missing values are random (MCAR) and do not introduce significant bias. However, this method may not work well for datasets with a high proportion of missing values or when patterns in the missing data need to be preserved. Advanced imputation methods like k-Nearest Neighbors (KNN) or predictive models can be used in such cases. Option A: Deleting rows with missing values can result in significant data loss, reducing the dataset's representativeness. Option C: Ignoring missing data leads to inaccuracies and potential errors in analysis. Option D: Filling with arbitrary constants like zero can distort the dataset, introducing bias. Option E: Duplicating rows compromises the dataset's integrity and can lead to overfitting in predictive models.

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