StellarMissing data is a common issue in data science and significantly impacts analysis and model performance. It occurs due to reasons like human error, incomplete data collection, or system failures. There are multiple ways to handle missing data, including removing it, filling it with appropriate values, or using predictive modeling. Below, we'll explore different approaches with code examples and their explanations.
If a dataset contains only a few missing values, removing the affected rows or columns can be a simple and effective solution. However, this method should be used cautiously, as it can lead to data loss.