To address the issue of missing values, we can try to impute data. Imputing can help to fill in missing data based on various statistical treatments and assumptions about its distribution. We explored imputing data using some of the most common imputation techniques: mean, median, most-frequent, uniform-and distance-kNN, and iterative. The simplest imputing strategies are mean, median and most-frequent, which replace missing values with the mean, median and mode of the available data, respectively. Mean imputing is most useful when the standard deviation of the distribution is relatively small and data is missing at random. On the other hand, median imputing is more suitable for distributions where there are outliers because it is less sensitive to them. Finally, most-frequent (or mode) imputing is not suitable for imbalanced distributions, as is the case for some of the features in our dataset. In kNN-based imputing strategies, missing values are completed using the mean of the k (usually five) nearest samples in the dataset. They leverage the similarity between instances and are effective when data points with similar attributes tend to have similar outcomes. The distance between samples can be measured in two ways. Uniform kNN imputing assigns equal weights to all neighboring points, whereas distance-based kNN imputing weighs neighbors by the inverse of their distance. Therefore, closer points will have a greater influence than those that are more distant. Iterative imputation, also known as multiple imputation, is an advanced technique that involves an iterative process to fill in missing values.