# Gremlin inference queries in Neptune ML
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As described in [Neptune ML capabilities](machine-learning.md#machine-learning-capabilities), Neptune ML supports training models that can do the following kinds of inference tasks:
+ **Node classification**   –   Predicts the categorical feature of a vertex property.
+ **Node regression**   –   Predicts a numerical property of a vertex.
+ **Edge classification**   –   Predicts the categorical feature of an edge property.
+ **Edge regression**   –   Predicts a numerical property of an edge.
+ **Link prediction**   –   Predicts destination nodes given a source node and outgoing edge, or source nodes given a destination node and incoming edge.

We can illustrate these different tasks with examples that use the [MovieLens 100k dataset](https://grouplens.org/datasets/movielens/100k/) provided by [GroupLens Research](https://grouplens.org/datasets/movielens/). This dataset consists of movies, users, and ratings of the movies by the users, from which we've created a property graph like this: 

![Sample movie property graph using the MovieLens 100k dataset](http://docs.aws.amazon.com/neptune/latest/userguide/images/movie_property_graph_example.png)


**Node classification**: In the dataset above, `Genre` is a vertex type which is connected to vertex type `Movie` by edge `included_in`. However, if we tweak the dataset to make `Genre` a [categorical](https://en.wikipedia.org/wiki/Categorical_variable) feature for vertex type `Movie`, then the problem of inferring `Genre` for new movies added to our knowledge graph can be solved using node classification models.

**Node regression**: If we consider the vertex type `Rating`, which has properties like `timestamp` and `score`, then the problem of inferring the numerical value `Score` for a `Rating` can be solved using node regression models.

**Edge classification**: Similarly, for a `Rated` edge, if we have a property `Scale` that can have one of the values, `Love`, `Like`, `Dislike`, `Neutral`, `Hate`, then the problem of inferring `Scale` for the `Rated` edge for new movies/ratings can be solved using edge classification models.

**Edge regression**: Similarly, for the same `Rated` edge, if we have a property `Score` that holds a numerical value for the rating, then this can be inferred from edge regression models.

**Link prediction**: Problems like, find the top ten users who are most likely to rate a given movie, or find the top ten Movies that a given user is most likely to rate, falls under link prediction.

**Note**  
For Neptune ML use-cases, we have a very rich set of notebooks designed to give you a hands-on understanding of each use-case. You can create these notebooks along with your Neptune cluster when you use the [Neptune ML CloudFormation template](machine-learning-quick-start.md) to create a Neptune ML cluster. These notebooks are also available on [github](https://github.com/aws/graph-notebook/tree/main/src/graph_notebook/notebooks/04-Machine-Learning) as well.

**Topics**
+ [Neptune ML predicates used in Gremlin inference queries](machine-learning-gremlin-inference-query-predicates.md)
+ [Gremlin node classification queries in Neptune ML](machine-learning-gremlin-vertex-classification-queries.md)
+ [Gremlin node regression queries in Neptune ML](machine-learning-gremlin-vertex-regression-queries.md)
+ [Gremlin edge classification queries in Neptune ML](machine-learning-gremlin-edge-classification-queries.md)
+ [Gremlin edge regression queries in Neptune ML](machine-learning-gremlin-edge-regression.md)
+ [Gremlin link prediction queries using link-prediction models in Neptune ML](machine-learning-gremlin-link-prediction-queries.md)
+ [List of exceptions for Neptune ML Gremlin inference queries](machine-learning-gremlin-exceptions.md)