Missing Values
Most real-world datasets have instances with values that are NaN or blank. These are missing values. The significance of missing values depends on multiple factors: the number of missing values, the number of features that have missing values, the tasks that are going to be carried out on data, and so on.
If the data is going to be fed into a machine learning model, then missing values should be dealt with. While some algorithms are capable of learning and predicting from data with missing values, it obviously makes more sense to train a model on data without missing values. This ensures that the model will learn relationships and patterns accurately.
Additionally, if there are many missing values or missing values in significant features of a dataset, they should also be dealt with.
There are two main ways to deal with missing values: deleting the instances or columns that have them (if they aren't significant), or imputing them with other values.
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