Example Concept: To handle missing values, you can use imputation methods like mean, median, or mode substitution, where missing values are replaced with the average, middle, or most frequent value of the column, respectively. Alternatively, you can use advanced techniques such as K-Nearest Neighbors (KNN) imputation, which predicts missing values based on the nearest data points. In some cases, removing rows or columns with missing values is also an option, especially if the proportion of missing data is small.