Hello everyone, welcome to my YouTube channel. Today, we will discuss how to combine data tables using Chat GBT.
Imagine that you have a bunch of details coming from different sources or the public, and you need to combine them into one table. In the past, you may have done some copy-pasting or written code in Python. But now, we can use Chat GBT to finish this work and automatically analyze it.
For example, let’s say we have a table that requires information about employees in a company, such as their ID, maximum bonus, salary adjustment, job tenure, title, and probability of resignation. We also have another table that records their ID, rent, satisfaction, and gender.
To combine these two tables, we just need to copy the data into Chat GBT and let it do the work. Once combined, we can use this table for refining predictions and machine learning data training.
Next, we can ask Chat GBT to analyze this table. For example, we can extract the relationship between the probability of resignation and the job title column. Based on the provided table, we can analyze the relationship between the probability of resignation and other factors.
After analyzing the data, we find that there doesn’t seem to be a strong direct relationship between the probability of resignation and other factors. However, we do find that certain job titles, such as ‘director,’ have a higher probability of resignation compared to titles like ’engineer’ or ‘assistant.’
In conclusion, Chat GBT not only allows us to combine data tables but also analyze the relationships between the data. This is extremely convenient for generating reports and gaining insights. Additionally, you can automate this process on your website or using the Open AI API.
If you want to learn more about the application of Chat GBT, please subscribe to my channel and share this video. Thank you for watching!
