> For the complete documentation index, see [llms.txt](https://manot.gitbook.io/manot/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://manot.gitbook.io/manot/product-guide/extracting-insights.md).
# Extracting Insights
Detecting Outliers and Edge Cases From Your Data
### Get Insights From Your Data
Once you have completed your data setup, you can extract insights from your data by detecting outliers and edge cases.
This code snippet below generates Manot's insights on outliers that your model will perform poorly on. Once it has completed processing, Manot will return an ID value which we will use in the next step to visualize the insights Manot has identified.
```
insight = manot.insight(
name="insight_example",
setup_id=setup["id"],
data_path="/path/to/data",
data_provider="local", # it can be "s3", "local" or "deeplake"
percentage="percentage" # percentage of images to be considered insight should be larger than 0 and less or equal than 100
)
```
## Visualize Your Data
Finally, we will use the ID generated in the previous step to visualize the outliers that Manot has identified.
```
insight_info = manot.get_insight(insight["id"])
manot.visualize_data_set(insight_info['data_set']['id'], deeplake_token)
```
*Note: Providing a Deep Lake token in the line of code below is only required if you used* [*Deep Lake*](/manot/product-guide/using-manot-with-activeloop.md) *as the data storage*
Example of output:
To see a full end-to-end example, visit our [Colab notebook](https://colab.research.google.com/drive/1QQ-xnShfpQx9skpzlT3u80n3lw_gM7Y-?usp=sharing#scrollTo=eab35eb2-5bdd-4715-a721-23fb188ca1ed).
