# Data List Page ## Preview The Data Preview option displays a sample of the actual data for the user to understand the data values in a better way. * Navigate to the ***Dataset*** list inside a Project. * Select either a Data Sandbox or Dataset from the displayed list. * Click the ***Preview*** icon for the selected data entity.

* The ***Preview Data Sandbox*** or Preview Dataset page opens based on the selected data.

## Data Profile This action helps users to visualize the detailed profile of data to know about data quality, structure, and consistency. A data profile is a summary of the characteristics of a dataset. It is created as a preliminary step in data analysis to better understand the data before performing an in-depth analysis. {% hint style="success" %} *Check out the illustration provided at the beginning to get the full view of the Data Profile page.* {% endhint %} {% embed url="" %} ***Displaying Data Profile for an added Feature Store*** {% endembed %} * Navigate to the Data list page. * Select a Dataset from the list. It can be anything from a Dataset, Data Sandbox file, or Feature Store. * Click the ***Data Profile*** icon.

* The ***Data Profile*** drawer opens displaying the Data Set information, Variable Types, Warnings, Variables, Correlation chart, missing values, and sample.

**Displaying the Data Profile for the selected Data**

## Create Experiment The *users can create a supervised learning (Auto ML) experiment using the **Create Experiment** option.* {% hint style="success" %} *Check out the illustration to create an auto ML experiment.* {% endhint %} {% embed url="" %} ***Creating an Experiment*** {% endembed %} * Navigate to the **Dataset List** page. * Select a ***Dataset*** from the list. * Click the ***Create Experiment*** icon. {% hint style="info" %} *Please Note: An experiment contains two steps:* * ***Configure**: Enter the Experiment name, Description, and Target column.* * ***Select Experiment Typ**e: Select an algorithm type from the drop-down menu.* * A ***Classification*** experiment can be created for discrete data when the user wants to predict one of the several categories. * A ***Regression*** experiment can be created for continuous numeric values. * A ***Forecasting*** experiment can be created to predict future values based on historical data. {% endhint %} * The ***Configure*** tab opens (by default) while opening the ***Create Experiment*** form. * Provide the following information: * Provide a name for the experiment. * Provide ***Description*** (optional). * Select a ***Target Column***. * Select a ***Data Preparation*** from the drop-down menu. * Use the checkbox to select a Data Preparation from the displayed drop-down.

**Selecting Data Preparation from the dropdown menu**

* Select columns that need to be excluded from the experiment. * Use the checkbox to select a field to be excluded from the experiment. {% hint style="info" %} *Please Note: The selected fields will not be considered while training the Auto ML model experiment.* {% endhint %}

**Selecting Columns to be excluded from the model training**

* Click the ***Next*** option.

**Configure tab with selected Data Preparations and excluded fields**

* The user gets redirected to the ***Select Experiment Type*** tab. * Select a prediction model using the checkbox. * *Based on the selected experiment type a validation notification message appears.* * Click the ***Done*** option.

* A notification message appears. * The user gets redirected to the ***Auto ML list*** page. * The newly created experiment gets added to the list with the ***Status*** mentioned as ***Started***.

## Data Preparation Data Preparation involves gathering, refining, and converting raw data into refined data. It is a critical step in data analysis and machine learning, as the quality and accuracy of the data used directly impact the accuracy and reliability of the results. The data preparation ensures that the data is accurate, complete, consistent, and relevant to the analysis. The data scientist can make more informed decisions, extract valuable insights, and unveil concealed trends and patterns within the raw data with the help of the Data Preparation option. * Navigate to the ***Data*** tab. * Select a Dataset from the list. * Click the ***Data Preparation*** icon.

* The ***Data Preparation*** page opens displaying the dataset in the grid format. * Select a column from the displayed dataset. * Open the ***Transforms*** tab. * Use the search tab to search for a transform. * Apply the required transforms on the data set. ***Delete rows with empty cell*** transform is applied to the SepalLength column.

* All the empty rows of the selected column are deleted. * Click the ***Save*** option. The Data Preparation will be saved with a default name by clicking the ***Save*** option.

* A notification message informs the users that the data preparation has been saved.

* The user gets redirected to the ***Data*** tab displaying the Data List. * Click the ***Data Preparation*** icon for the same dataset.

* The ***Preparation List*** drawer appears with the saved data preparation. You can use the ***Create Preparation*** option to create a new preparation based on the same Data.

{% hint style="info" %} *Please Note: Refer to the* [***Data Preparation***](https://app.gitbook.com/s/e7P9Uf1O3iaFlsO3hfWI/data-center/data-preparation) *section of the* [***Data Center***](https://app.gitbook.com/o/BHXEmSpD7W3xDgxnOIpk/s/e7P9Uf1O3iaFlsO3hfWI/) *module for more details.* {% endhint %} ## Delete * Navigate to the ***Data*** tab. * Select a Dataset from the list. * Click the ***Delete*** icon.

* A dialog box opens to ensure the deletion. * Click the ***Yes*** option. ![](https://3817372244-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fz33KQNYQvBTgQKJBgwTz%2Fuploads%2FR6ADa6oYHv4q5XEAPOr1%2Fimage.png?alt=media\&token=e298d69f-bc69-459f-ba37-db10a33ff298) * A notification message appears to assure about the completion of the deletion action. * The concerned Data set will be removed from the list.

{% hint style="info" %} *Please Note: The Preview, Create Experiment, and Data Preparation Actions are not supported for the Datasets based on a Feature Store.* {% 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/data-science-lab-5/tabs-for-a-dsl-project/data/data-list-page.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.