Found 617 results.
Run every mile at Kovai MarathON to support the cause of promising young students’ education through our Kovaion Kalvi!
The Test Data Generation Tool we are discussing in this blog is a Python script that generates test data for a client, who is a law firm. The script can generate datasets on beneficiary details and case details.
Kovaion offers a variety of services in AI and Data Analytics. The application we are about to discuss in this Test Data Generation Tool is a Python script that generates test data for a law firm client. The script can generate datasets on beneficiary details and case details. The script uses Numpy and Pandas libraries to extract values from a master table based on specific conditions. The script takes the user’s input, such as the number of rows and columns required from the master table, and generates test data accordingly.
The Test Data Generation Tool works by using a master table that contains values with respective percentages for the test data. The master table can be a CSV or Excel file. The app then extracts the required data from the master table based on the conditions specified by the user.
The conditions can be nested, which means that one condition can depend on another condition. For example, if the user wants to generate test data for a specific range of dates, they can specify the start and end dates. The app then generates test data for all the dates within that range. Similarly, if the user wants to generate test data for a specific range of values for a particular column, they can specify the start and end values.
The app uses Numpy and Pandas libraries to manipulate the data extracted from the master table. Numpy is used for numerical operations, such as generating randomly populated values by scaling the percentages into probabilities. Pandas is used for data manipulation in a data frame, such as merging multiple tables and filtering rows based on specific conditions. Consider a record of 1000 values with the field Entity which should be split into two categories Entity 1 – 60% and Entity 2 – 40%. Further, the data is split based on nested conditions such as Beneficiary Type, Country, and so on. The following table shows how the data is split based on the field Entity (Accuracy = 98.4%).
The app has several benefits for clients who need to generate test data. Some of the Test Data Generation Tool benefits are:
The app generates test data quickly and efficiently, saving time and effort for clients.
The Test Data Generation Tool allows users to specify the exact conditions and values required for the test data.
The app can be used multiple times to generate test data, making it a valuable tool for various applications.
Overall, the Test Data Generation Tool we have discussed in this blog post can be a powerful tool for generating high-quality test data quickly and efficiently. By leveraging the power of Python- NumPy, and Pandas, the app allows users to extract data from the master tables based on complex conditions. We from Kovaion have built this application to such a degree that we can customize the dataset values to the user’s requirements. Kovaion is coming up with many more solutions to help businesses to simplify their needs.
Author – Afridi Baig J, Jr Data Analyst
Kovaion will assist your business to implement AI & ML Services effectively.
Read MoreThis website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.
Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings.
If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again.
