There are a few ways to deal with sparse data:
– One way is to simply ignore the data that is missing. This can be done by either discarding the entire row or column that contains missing data, or by imputing the missing values with the mean, median, or mode of the remaining values.
– Another way to deal with sparse data is to use a technique called feature selection, which essentially means choosing a subset of the features to use in the model. This can be done using a variety of methods, such as forward selection, backward selection, or a combination of the two.
– Finally, you could also use a technique called feature engineering, which involves creating new features from the existing data. For example, you could combine two or more features to create a new feature that is less likely to be sparse.
