Data Preparation
Data preparation is the process of collecting, cleaning, and transforming raw data into a format that can be easily analyzed and used for various applications.
Data Preparation involves gathering, refining, and converting raw data 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 is to ensure that the data is accurate, complete, consistent, and relevant to the analysis. By using this action, the data scientist can make more informed decisions, extract valuable insights, and unveil concealed trends and patterns within the raw data.
Please Note: The BDB Data Science Lab provides an option to access this functionality on the Dataset List page.
Navigate to the Dataset list page.
Select a Dataset from the list.
Click the Data Preparation icon.
The Data Preparation page opens displaying the dataset in the grid format.
Apply the required transforms on the data set. E.g., Auto Prep option is applied on the displayed Dataset (The user can also use any transformation using the Transforms tab).
Since Auto Prep is selected the below screen appears.
Select or reject the listed transforms by using the checkbox under the Transformation List that appears for the Auto Prep option.
Click the Proceed option.
Provide a name for the Data Preparation.
Click the Back icon to go back.
While clicking the Back icon, a notification message appears to inform the users that the data preparation has been saved.
The user gets redirected to the Dataset list page.
Select the Dataset for which the Preparation was saved.
Click the Data Preparation icon for the same Dataset.
While opening the Dataset it redirects to the Preparation List displaying the saved data preparation.
Please Note:
The details on how to use the Data Preparation option are described in the Data Preparation section under the Data Center.
Refer the Data Science Lab Quick Start Flow page to get an overview of the Data Science Lab module in nutshell.
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