> For the complete documentation index, see [llms.txt](https://pycaret.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://pycaret.gitbook.io/docs/get-started/modules.md).

# Modules

{% tabs %}
{% tab title="Supervised ML" %}

### [Classification](/docs/get-started/quickstart.md#classification)

In machine learning, classification refers to a predictive modeling problem where the target to be predicted is a *class label.*

* [Quickstart](/docs/get-started/quickstart.md#classification)
* [API Docs](https://pycaret.readthedocs.io/en/latest/api/classification.html)
* [Tutorial](https://nbviewer.org/github/pycaret/pycaret/blob/master/tutorials/Tutorial%20-%20Binary%20Classification.ipynb)

### [Regression](/docs/get-started/quickstart.md#regression)

In machine learning, regression refers to a predictive modeling problem where the target to be predicted is a *continuous variable*.

* [Quickstart](/docs/get-started/quickstart.md#regression)
* [API Docs](https://pycaret.readthedocs.io/en/latest/api/regression.html)
* [Tutorial](https://nbviewer.org/github/pycaret/pycaret/blob/master/tutorials/Tutorial%20-%20Regression.ipynb)
  {% endtab %}

{% tab title="Unsupervised ML" %}

### [Clustering](/docs/get-started/quickstart.md#clustering)

Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups.

* [Quickstart](/docs/get-started/quickstart.md#clustering)
* [API Docs](https://pycaret.readthedocs.io/en/latest/api/clustering.html)
* [Tutorial](https://nbviewer.org/github/pycaret/pycaret/blob/master/tutorials/Tutorial%20-%20Clustering.ipynb)

### [Anomaly Detection](/docs/get-started/quickstart.md#anomaly-detection)

Anomaly detection is identifying data points in data that don't fit the normal patterns. It can be useful to solve many problems including fraud detection, medical diagnosis, etc.

* [Quickstart](/docs/get-started/quickstart.md#anomaly-detection)
* [API Docs](https://pycaret.readthedocs.io/en/latest/api/anomaly.html)
* [Tutorial](https://nbviewer.org/github/pycaret/pycaret/blob/master/tutorials/Tutorial%20-%20Anomaly%20Detection.ipynb)
  {% endtab %}

{% tab title="Time Series" %}

### [Time Series](/docs/get-started/quickstart.md#time-series)

Time series forecasting is the process of analyzing time series data using statistics and modeling to make predictions and inform strategic decision-making

* [Quickstart](/docs/get-started/quickstart.md#time-series)
* [API Docs](https://pycaret.readthedocs.io/en/latest/api/time_series.html)
* [Tutorial](https://nbviewer.org/github/pycaret/pycaret/blob/master/tutorials/Tutorial%20-%20Time%20Series%20Forecasting.ipynb)
  {% endtab %}

{% tab title="Datasets" %}

### [Datasets](https://pycaret.readthedocs.io/en/latest/api/datasets.html)

Module in PyCaret containing ML datasets. [Learn More](https://pycaret.readthedocs.io/en/latest/api/datasets.html).
{% endtab %}
{% endtabs %}


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://pycaret.gitbook.io/docs/get-started/modules.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.