> For the complete documentation index, see [llms.txt](https://nag-9-s.gitbook.io/machine-learning/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://nag-9-s.gitbook.io/machine-learning/input-data-structure.md).

# input data structure

the requirements for input data structure for each advanced analytics task in MLlib.

In the case of classification and regression, you want to get your data into a column of type `Double` to represent the label and a column of type `Vector` (either dense or sparse) to represent the features.

* In the case of recommendation, you want to get your data into a column of users, a column of targets (say movies or books), and a column of ratings.
* In the case of unsupervised learning, a column of type `Vector` (either dense or sparse) is needed to represent the features.
* In the case of graph analytics, you will want a DataFrame of vertices and a DataFrame of edges.

The best way to do this is through transformers.