Eras Tour Song Analysis

So, we're on Kaggle, and here's this Taylor Swift Eras Tour dataset. I've already downloaded that data here, so let's just open up our AI tool, Devra. Go to the Kaggle projects that we've set up, add a task, and very simply, we're going to say, "Create an analysis of the Taylor Swift Eras Tour dataset." Hit create and start, and see what comes up.

We just want the starting point. This is not going to be an earth-shattering kind of analysis. It's looking at the dataset right now, examining the column names, and identifying what kind of data is in each column. From this point, it's going to create a very fundamental proposal.

And here's that proposal. The next step is typically a placeholder Python notebook. Okay, so here it has that Python notebook that's come up. Let's say yes, create the file. See, that file has now popped up here, and let's run the notebook.

So, this is our basic analysis. We've run it. It's got an import, and it loads the data. We check out that data in there with the head. Then there's a data exploration where we describe the dataset. There's a data cleaning step, followed by a quick descriptive analysis for the numeric columns and a correlation analysis.

There is an error here, but that's not a big deal because if ever there's an error, you just copy it, paste it into Devra, say no, the task isn't complete, and it's going to come up with a fix. Okay, it's come up with a fix. It's asking to update the file. We do update it, reload, rerun, and now the correlation analysis is fixed. The error is corrected.

We have a feature analysis, energy by genre. That's great. The basic point is to look at all this. There are so many fundamental basic analyses performed that we didn't have to do—from the importing and the loading to the cleaning and correlation analysis. This might give us an idea for a future comparison that we can do. And all this was done with the Devra tool.

So, go to Devra to download the tool. Also, the link is in the description for this particular analysis. Thanks!