Handling missing data begins with preprocessing. Imputation methods, such as replacing missing values with the mean, median, or mode, or using advanced techniques like KNN imputation, fill gaps in the dataset before training.
Neural networks can handle missing data directly using masking layers that ignore specific inputs during computation. For time-series data, RNNs or Transformers can infer missing values based on temporal patterns.
Data augmentation techniques or feature engineering may also help mitigate the impact of missing data. Analyzing the proportion and distribution of missing values guides the choice of handling strategy to minimize bias and maximize model performance.