Unfortunately, this task can become incredibly time-consuming - especially when you don’t know the right syntax to use to get the desired result. When performing exploratory data analysis, generating a quick visualization in Python often helps me better understand the dataset. You can still follow this tutorial with the free version of ChatGPT that uses GPT-3.5 in the backend.ģ Ways to Automate Data Science Workflows with GPT-4 and Python It’s okay if you don’t currently have access to GPT-4. Once you get access to the API, you can follow this guide to use it in Python. If you don’t want to pay the monthly subscription fee, you can also join the API waitlist for GPT-4. There is a monthly subscription fee of $20/month that can be canceled anytime: To access the model, you need to upgrade to ChatGPTPlus by clicking on “Upgrade to Plus.”
GPT-4, on the other hand, is currently hidden behind a paywall. Simply navigate to, fill out the required details, and you will have access to the language model: GPT-3.5 is publicly available on OpenAI’s website.
If you’d like to follow along with everything I do in this article, you need to have access to GPT-4 and GPT-3.5. I will create the same prompts and feed them into both GPT-4 and GPT-3.5, to see if the former indeed does perform better and result in more time savings. In this article, I will show you how I use ChatGPT to automate data science workflows. Were there any significant benefits to using GPT-4 over its predecessors? Would it help me save more time than I already was with GPT-3.5? Once GPT-4 was released, I was curious about whether it would make a difference in the work I was doing. I used the tool to save the amount of time spent on menial work - so that I could focus on coming up with new ideas and creating better models instead. I spend a huge portion of my day extracting data from different file formats and consolidating it in one place.Īfter ChatGPT was first launched in November 2022, I looked to the chatbot for some guidance with my daily workflows. OpenAI’s latest model is harder to trick and does not confidently generate falsehoods as frequently.Īs a data scientist, my job requires me to find relevant data sources, preprocess large datasets, and build highly accurate machine learning models that drive business value. GPT-4, on the other hand, was trained to “hallucinate” less often. The algorithm went on to corroborate its response with made-up facts that almost had me convinced for a moment. One user took to Twitter after the model insisted that elephant eggs were the largest of all land animals:Īnd it didn’t stop there. ChatGPT, the chatbot which was built on top of GPT-3.5 and released to the public, was notorious for “hallucinating.” It would generate responses that were seemingly correct and would defend its answers with “facts”, although they were laden with errors.
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