As a data scientist, I find data preprocessing to be the most tedious part of the job. Can Generative AI (ChatGPT, Gemini) effectively automate the writing of complex SQL queries or Python scripts for feature engineering on messy, unorganized datasets?
3 answers
In the realm of Data Science, Generative AI (ChatGPT, Gemini) is incredibly useful for bootstrapping your preprocessing scripts. I’ve used it to generate regex patterns for string cleaning and to suggest creative feature engineering ideas based on the column descriptions of a dataset. While it shouldn't replace your exploratory data analysis (EDA), it can definitely write the boilerplate code for pandas or PySpark transformations much faster than a human can. I recently saved two days of work by having the AI generate a script to handle nested JSON structures that were inconsistent across millions of records.
Donna, do you find that Generative AI (ChatGPT, Gemini) struggles with highly specialized domain data, like medical or financial records, during feature engineering?
It’s also great for generating synthetic data to test your models when the real dataset is too small or restricted by privacy laws.
Sandra makes a great point. We used the AI to generate 10,000 synthetic customer profiles last month to validate our recommendation engine's performance before going live.
Thomas, you hit the nail on the head. For domain-specific tasks, the AI needs context. If I'm working on clinical data, I have to provide the AI with a summary of the medical terminology involved. Once it has that "dictionary," its ability to suggest relevant features improves dramatically. It won't replace the subject matter expert, but it acts as a brilliant sounding board for brainstorming what features might actually correlate with the target variable.