Answer / Sidharth Sharma

1. Removing (Deleting) Rows: This method involves removing any row with at least one missing value. However, this can lead to loss of valuable information and potentially biased results.
2. Mean or Median Imputation: Replacing each missing value in a column with the mean or median of the column's non-missing values. This method assumes that all missing values are randomly distributed.
3. Regression Imputation: Using a regression model to predict the missing values based on other available features in the dataset. This can lead to improved accuracy compared to simple imputation methods, but requires more computational resources and careful model selection.
4. Multiple Imputation: Creating multiple completed datasets (each with different imputed values) and combining the results from each dataset using appropriate statistical techniques. This method helps account for the uncertainty associated with missing data.

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