Dealing with Sparse Datasets in Machine Learning

This article was published as a part of the Data Science Blogathon. Missing data in machine learning is a type of data that contains null values, whereas Sparse data is a type of data that does not contain the actual values of features; it is a dataset containing a high amount of zero or null values. It is a different thing than missing data. Sparse datasets with high zero values can cause problems like over-fitting in the machine learning models and several other problems. That is why dealing with sparse data is one of the most hectic processes in machine learning.