After dealing with missing data in your dataset. You will most likely face Categorical Features in numerous datasets. In the majority of cases, these features tend to be non-numerical and thus need to be converted to be processed in machine learning algorithms.
When working on data science projects, it’s very likely that you’ll be encountering missing data in your columns. It’s not ideal to disregard or take out all the rows containing missing data for any project. Other columns for the same row where the data is missing can be critical for the data preparation state, so it’ll be wiser to infer or find a way to fill in the missing values in our dataset for a better outcome.