Explanation

Answer Example

When handling missing data, I first analyze the extent and distribution of missing values. If the missing data is minimal, I may exclude those records to avoid introducing bias. For larger gaps, I use imputation techniques like mean, median, or predictive modeling, depending on the nature of the data. For example, in a customer segmentation project, I used k-Nearest Neighbors imputation to fill missing age values, ensuring the dataset remained complete without compromising the analysis. This approach allowed me to deliver accurate insights while maintaining data integrity.