Hi everyone, thanks for joining today. We’re going to be doing a demo on Advanced Data Analysis. This tool, formerly known as Code Interpreter, is a powerful tool for data interpretation and visualization. Let’s go ahead and show you how to set this up.
To set up Advanced Data Analysis, you need to come here to Beta features. The name Advanced Data Analysis is much more fitting than what it used to be as Code Interpreter because it does a lot more than just interpret code. It can actually write and execute Python code. It also works with uploading files, data analysis, image conversions, file conversions, and so much more.
Now, let’s get into the demo. The data set we’re going to use came from Kaggle. It’s a used car data set that came from Craigslist. It’s super helpful and has a lot of great data. We’ve got a data set here of about 10,000 cars that we’re going to pull into Chat GBT. Let’s go ahead and load that data in.
Once you enable the feature, you’ll see a plus sign here. Here are the cars. There are 10,000 cars in this data set.
Now that the data is loaded, let’s ask our first question. Our first question is, ‘What is this data?’ It actually executes code to give us the full answer. It tells us the ID, URL, region, and all the elements in the actual table, such as the odometer, vent, drive, and size. It’s pretty cool as it gives you a full analysis of the data.
Next, we’re going to ask, ‘What are some interesting questions that I could ask about this data?’ The answer provided includes pricing analysis, car characteristics, and geographic analysis. These are all great potential questions.
The first thing we’re going to ask is, ‘Please visualize the number of cars per year in a graph, excluding any year that has less than 100 cars.’ It executes Python code to read in the data and create the graph. The graph shows the number of cars per year in a bar chart.
The next question we’re going to ask is, ‘Is there a correlation between the state region and the average price of cars listed?’ The answer explains the steps to do this correlation and displays the information in a visual format.
We then ask, ‘How have car prices evolved over time?’ The answer provides a visual representation of the prices.
Next, we ask, ‘Please create a pie chart showing the distribution of car conditions.’ The answer provides a simple pie chart to understand the conditions of the vehicles.
We also ask, ‘Can we identify any trends or patterns in the description based on the manufacturer and model?’ The answer explains the steps it will take to identify and visualize the trends or patterns.
Finally, we ask, ‘What time of year are most cars listed?’ After several attempts, it finally provides the answer that most cars were listed in January and February.
Chat GBT is an amazing tool that can analyze data and provide valuable insights. It’s like an assistant that never gives up and keeps trying until it finds the answer. The real value of this tool is evident as you analyze data with Chat GBT.
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