How do you handle missing data in a dataset?

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Handling missing data is a crucial step in data preprocessing. There are several strategies depending on the nature and extent of the missing values:

1. Remove Missing Data:

   • Delete Rows: If only a few rows have missing values and the dataset is large, you can drop those rows.

   • Delete Columns: If a column has too many missing values (e.g., over 50%), it may be best to remove the column entirely.

2. Impute Missing Data:

   • Mean/Median/Mode Imputation: Replace missing numerical values with the column mean or median; use mode for categorical data.

   • Forward/Backward Fill: In time series data, propagate previous or next values to fill gaps.

   • K-Nearest Neighbors (KNN): Use values from similar records to impute missing data.

   • Multivariate Imputation: Use regression or advanced models (e.g., MICE – Multiple Imputation by Chained Equations) to predict missing values based on other features.

3. Use Algorithms That Handle Missing Data:

   • Some machine learning algorithms (e.g., decision trees, XGBoost) can handle missing values internally.

4. Add Missing Indicators:

   • Create an additional binary feature to indicate whether data was missing. This can sometimes help models detect patterns related to missingness.

The choice of method depends on the dataset's size, the type of data, and the importance of the missing values. Always analyze the pattern of missingness—whether it’s random or systematic—before deciding how to handle it.

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