Trying out the Noteable Plugin for ChatGPT Plus

[Music] all right hi everyone I'm Ben welcome back to the data literacy video channel so we're going to go through today the notable app plugin that's now available on the chat GPT website for chat GPT plus subscribers this video is a follow-up to the code interpreter review that we did last month where we showed how these new llm tools are revolutionizing data and data analytics so without further Ado let's go ahead and Dive Right In to what we see here with the notable plugin okay so here we are in chat GPT I'm selecting gpt4 and the plugins beta I'm choosing the notable plugin which here you can see is checked okay so in order to test this out a little bit here's what we're going to do we're going to go to this site on data.gov this is electric vehicle population data in my home state of Washington currently happens to be the most popular data set on the entire platform and we're going to use that CSV we are going to also use this project which I just created here in my notable my free notable account it's a brand new project that we're going to use to create notebooks within it in order to do some analysis okay so there we have it data set and project we're going to go ahead and create our prompt so I'll tell the notable plugin on chat GPT use this data okay and I'm going to copy the link address to the CSV here in data.gov paste it here into the chat I'm going to hit shift return on my Mac keyboard so that it doesn't execute the prompt but moves it down a line I'll say use this project I'll go back to my notable project I'll click the URL up the top up here and paste that into there I'll hit shift enter a couple more times as well to give me some space now what I'm going to do is enter this prompt that I have Elijah Meeks the CTO of notable to thank so he walked me through the plugin he gave me this as a starting point to try something kind of like this so what is it asking the plugin to do it's asking the plugin to do a data driven analysis of this electric vehicle data my audience is a Tesla sales representative who's looking to convince people to purchase a Tesla over another make of electric vehicle please include in the notebook a description about what you're doing please do some machine learning along with basic visual analytics to identify Trends and anomalies also do some exploratory data analysis and give me some data driven suggestions so we're going to copy this prompt we're going to paste this here into the bottom of this and we'll hit send so now what is going to happen here is chat GPT is going to start by requesting that project that I gave it okay and the next thing it's going to do is you're going to see is it's going to create a notebook within it there it is EV analysis if we click on that we're going to be able to keep track of what chat GPT is doing and this is I think why there's some value over code interpreter as Elijah helped me understand code interpreter is going to create that code right there in chatgpt and it's going to create the python code but the notable plugin is actually going to put it into the notebook right automatically there we go it just put this entire header in here electric vehicle data analysis we're just watching it play out live right that's exactly what we can see in these drop downs if we were to look closely we'd see that this is what actually chat GPT is doing is it's loading the data set it's creating a header it's giving me a preview of the variables and what's in it I can see it's got you know information about each vehicle where it's located it's make and model and so forth I can also see over in the chat GPT side a printout of the table at least the first few rows of it you know it's giving me that same output over here on the chat GPT side but the value of having it here in the notebook is I could share this I could send this to and collaborate with others I can make it public maybe and I'll put the link in the in the YouTube video description so even you can go check it out if you want and so you can see how it works right and we can leave comments you could go in and change it and edit it now also what it's doing now is it's giving me some some basic idea of the overall shape of the data so what is it it's got a 130 000 rows and 17 columns now it's giving me the type of data of each column so this is what we call data profiling or basically what I call sometimes exploring the Contours of the data it's telling me how many nulls or missing values I can see in each column so I might want to look out for why the model might be missing or why it might not have a legislative district and so now it's going to give me some basic descriptive statistics of the quantitative variables or what it calls the numerical columns so counts mins Maxes medians for the different quantitative variables you know sometimes that might not be always valuable like a postal code what's the average postal code I don't know I don't need to know but it might be interesting the the model years mins and Maxes maybe the average MSRP or the median could be useful and interesting so again just basic descriptive statistics of this data set I'm learning so much about what's already there now we get to some interesting stuff stuff it's actually going to plot the model here here on this histogram right so we can see it's doing that it's putting and I can see this is the shape of it I can see the distribution here the histogram for the electric range I can see also a breakdown of the top 10 makes so of course Tesla being the most then Nissan Chevy and Ford coming in a distant second third and fourth and so there we have it right some basic statistics I can see all the output here too it looks like it's not quite done yet now it's going to do basically some machine learning it looks like it's actually splitting the data into a training version and a test version and it looks like it's going to do some type of perhaps some regression in here and so let's see what it does here as it uh follows my prompt and asks as I ask it to actually go through some machine learning steps and it's giving us it looks like the root mean squared error perhaps of this random Forest regressor it looks like what it's actually doing so it's coming out with most common makes and models together and giving us some you know more information here so it's just amazing to me you know what we're seeing and we could always come in here and we can edit every anything in here we could change we could change the color we could edit the code pretty quickly and easily this is just python right so if we know how to write python we can program we can modify what we're seeing in here make some changes to it so there we have it right so I'll stop here I mean this to me is already uh pretty pretty fascinating it looks like here we're seeing some um overall recommendations coming out of it right based on these findings a Tesla sales representative could emphasize the popularity of Tesla vehicles the high average electric range and the superior performance of Tesla models in terms of electric range these are all strong selling points so it's already trying to interpret what it's seeing here and you know explain the value of that to someone who's trying to sell a Tesla so I think that that's just a really interesting overall you know kind of output and I think it's something that is pretty compelling the fact that you now with just a single prompt I mean I've just gotten started we can continue to the conversation we could ask it to do more analysis we could ask it to make changes to what it's already done and of course we can share and send this exact notebook to anyone who can take a look at it too you know this to me is again you know it's pretty amazing and of course there are a lot of possible errors in here um you know it's an llm that's interpreting my prompt and maybe getting it right it may not it's converting that into uh python it's converting that into markdown and putting it all into a notebook you know here it is out on the public web so pretty remarkable plug-in you know my hat goes off to the folks at notable Elijah and team for creating this so quickly I think this one is the kind of thing that we're going to see is just really going to overhaul what the day in the life of a data analyst is like in the coming year and Beyond okay so there you have it we'll do more of these we'll try to you know continue to see how these emerging llms are affecting data analysis I think it's a powerful technology that's going to change the way we do uh analysis going forward and so we'll pause there alright everyone be sure to subscribe leave your comments here and let me know what you think let me know what questions you might have we're going to take that into consideration in future videos that we do here on the data literacy video channel alright everyone take care we'll talk to you soon bye for now

Channel: Data Literacy

Views: 984

Keywords: data literacy, data, data visualization, data science, data analytics, data analysis, data analyst, data literacy basics, chatgpt, noteable, noteabe plugin, chatgpt plus, llm, openai, notebook

Id: b48NCYLkawk

Length: 9min 18sec (558 seconds)

Published: Thu Jun 01 2023