Example Concept: One common method for handling missing data is imputation, where missing values are replaced with estimated ones. Techniques include mean, median, or mode imputation for numerical data, and the most frequent category for categorical data. More advanced methods involve using algorithms like k-Nearest Neighbors (k-NN) or regression models to predict missing values based on other available data. It is crucial to assess the impact of these methods on the dataset's integrity and the model's performance.