What are the common causes of missing data in data science projects? What techniques can be used to handle missing data effectively? How do methods like deletion, imputation, and interpolation differ from each other? What impact does missing data have on model performance and accuracy? How can a data scientist decide the best approach for handling missing values in a dataset?