"Handling missing values depends on the dataset and business context. First, I would analyze the percentage of missing data using Pandas (df.isnull().sum()). If missing values are minimal, I might drop those rows (df.dropna()). If they are significant, I would use imputation techniques, such as replacing them with the mean, median, or mode for numerical data (df.fillna(df.mean())). For categorical data, I could use the most frequent value or create a separate category. If the data follows a pattern, I might use predictive modeling techniques like regression or KNN imputation. Choosing the right method depends on the dataset's impact on analysis."