Data List Page
This section of the document describes the actions attributed to the added data inside a Data Science Lab project.
This section of the document describes the actions attributed to the added data inside a Data Science Lab project.
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.
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.
Check out the illustration provided at the beginning to get the full view of the Data Profile page.
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.
The users can create a supervised learning (Auto ML) experiment using the Create Experiment option.
Check out the illustration to create an auto ML experiment.
Navigate to the Dataset List page.
Select a Dataset from the list.
Click the Create Experiment icon.
Please Note: An experiment contains two steps:
Configure: Enter the Experiment name, Description, and Target column.
Select Experiment Type: 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.
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.
Select columns that need to be excluded from the experiment.
Use the checkbox to select a field to be excluded from the experiment.
Please Note: The selected fields will not be considered while training the Auto ML model experiment.
Click the Next option.
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 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 Preparation List window displays the preparation based on the selected Excel file. The user may use any of the displayed data preparation from the list.
The user can select a sheet name from the given drop-down menu.
Click the Data Preparation option to create a new preparation.
The Data Preparation page opens displaying the dataset in the grid format.
Click the Auto Prep option to apply the default set of transforms under the Auto Prep.
The Transformation List window opens.
Select or dis-select the transforms using the given checkboxes.
Click the Proceed option.
The selected AutoPrep transforms are applied to the dataset. Provide a name for the Data Preparation.
Click the SAVE option.
A notification message informs the users that the data preparation has been saved.
The user gets redirected to the Preparation List window.
Click the Refresh icon.
The newly created Data Preparation gets added to the Preparation List.
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.
A notification message appears to assure about the completion of the deletion action.
The concerned Data set will be removed from the list.
Please Note: The Preview, Create Experiment, and Data Preparation Actions are not supported for the Datasets based on a Feature Store.
Please Note: Refer to the Data Preparation section of the module for more details.
