Concerns about fairness in data aren't new; they've been around long before AI became a big player. It's not simply a matter of dealing with biased data; the quality of how we handle and process this data is equally important. Fairness demands a comprehensive and thoughtful approach to both data itself and the processes that shape its interpretation and utilization.

One major issue about data quality is the presence of missing values. When these gaps exist in the dataset, they can disrupt the seamless flow of data through various processes in the analytical pipeline, such as data visualization. Missing values can also affect the predictive power of machine learning methods. This mini project explores machine learning imputation techniques to address the presence of missing values. As it turns out, when handling missing values, iterative imputation outperforms single imputation even without hypertuning on validation and holdout sets.