# Algorithms
{% hint style="success" %}
*Pre-requisite:*
1. **Configure the Algorithms** using the [**Admin module**](/bdb-documentation/core-platform/platform-administration/admin-panel-options/configurations/ds-lab-settings.md) to access them under the [**Data Science Lab Project**](/bdb-documentation/data-science-lab/project/creating-a-project.md) creation*.*
2. The user must select Algorithms while creating a Project to make them accessible for a Notebook within the Project.
{% endhint %}
{% hint style="info" %}
*Check-out the walk through on how to apply Algorithms inside Notebook.*
{% endhint %}
The entire process to access the Algorithms option inside the DS Lab and actually create a model based on the Algorithm is a three step process:
1. [**Admin Settings for Algorithms**](#admin-settings-for-algorithms)
2. [**Project Level Algorithm Selection**](#project-level-algorithm-selection)
3. [**Using Algorithm inside a Notebook**](#algorithms-tab-inside-notebook)
### Admin Settings for Algorithms
* Navigate to the ***Admin*** module.
* Open the ***Notebook Settings*** option from the ***Configuration*** section of the ***Admin*** panel.
* The Notebook Settings page opens.
* Select the ***Algorithms*** using the drop-down option.
* Click the ***Save*** option.
* A confirmation message appears to inform about the Notebook details updates.
### Project Level Algorithm Selection
Once the Algorithm settings is configured in the Admin module, the user can access the Algorithm within the Data Science Lab.
* Navigate to the ***Data Science Lab***.
* Click the ***Create Project*** option.
* Together with the other required fields select the algorithms using the given checkboxes from the drop-down menu.
* The selected Algorithms appear on the field separated by comma.
* ***Save*** the project.
### Using Algorithms inside a Notebook
Once the Algorithms are selected while creating a Project, those algorithms will be available for all the Notebooks created inside that project.
* Open the ***Notebook tab*** inside the ***same Project.***
* ***Create a new Notebook*** or ***Navigate***** to a*****n existing Notebook*** under the same Project.
* You can see ***the selected Algorithms at the project level*** get listed under the right-side panel of the Notebook.
* Add a code cell with the dataset information.
* Define the ***Dataframe*** using another code cell.
{% hint style="info" %} Please Note: You can run the cell containing the data frame details to see the data output below:
{% endhint %}
* Add another code cell below.
* Select an algorithm type by using the checkbox.
* You will get the algorithm script pasted in the intended code cell.
* Specify train and test data.
* Run the cell.
* You can see the data output below.
* Add a new code cell below.
* Click the ***Save Model*** option.
* Specify the model name and model type in the auto generated code.
* Run the cell.
* The model gets saved and lists under the Models tab (The model will list under the ***Unregistered*** models as it is not registered yet).
* Add new code cell.
* ***Load the model*** in the cell by clicking the checkbox given next to the model.
* Specify the model as the loaded model and model type for the loaded model.
* Run the cell.
* The data output gets displayed below.
#### Registering an Algorithm based Model
This section displays the flow on how to register a saved model from a Notebook.
* Click the ***Register*** option for the saved model.
* The ***Register Model*** dialog box appears to confirm the action.
* Click the ***Yes*** option.
* A confirmation message appears to inform about the completion of the model registration action.
* The concerned model appears under the ***Registered*** model list.
* Open a Pipeline using the ***Data Pipeline module*** that contains the ***DS Lab Model Runner component***.
* You can see the same model (which was registered from the DS Lab module) listing under the ***Meta Information*** tab of the ***DS Lab Model Runner*** component.
{% hint style="info" %} Please Note: The model based on an Algorithm script can be registered as an API service. You can refer to the [***Publish a Model as an API Service***](/bdb-documentation/data-science-lab/various-tabs-to-work-with/model/register-model-as-an-api-service.md) section for more details.
{% endhint %}
---
# Agent Instructions: 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:
```
GET https://docs.bdb.ai/bdb-documentation/data-science-lab/various-tabs-to-work-with/notebook/notebook-page/notebook-operations/algorithms.md?ask=
```
The question should be specific, self-contained, and written in natural language.
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.