Chicago real estate AI python analysis

All right, it's morning time. My shirt's inside out. Let's do a little data science using Devra AI on this Kaggle page for some Chicago real estate data. I've downloaded it here. We'll go to Devra, go to our demos that we've set up as a project, and we'll add a task asking it to analyze it and create a Python notebook that is an analysis of the Chicago real estate data. Now we'll hit create and start. Let's just see what happens.
The idea here is that it's going to go through all the files and see what's there. In this case, I just want it to get the project started. I don't really care if the analysis is incredibly good; I just want it to handle all the library importing, the file importing, and some basic data structure setup.
Okay, so here's the proposal. Let's just have it go with that. It looks like it's creating a really rudimentary file. Now it wants to update that file with more detail. Here's the file it has made and updated. Let's just launch it and see what it did.
Here's the notebook. Let's see what it did. It's importing our libraries, setting up the plot, loading our data, and doing a basic output of some of the data. By the way, this is all possible because the first thing Devra did was inspect the data file to see the columns that are there so that it knows how to call it and what to expect.
A little data cleaning. Amazing. We're seeing a basic description of the statistics. That's awesome. Distribution of property types, price trends over the years. Let's see here. Some feature engineering, some basic analysis of average price by property type, and then it writes up a conclusion. It did all this in a matter of seconds.
The thing is, it might not be the analysis that we want, but look at all the legwork that it did for us. We could always ask it to improve this analysis. We can ask Devra to add an analysis or alter an analysis, or you can go in and change it yourself. You didn't have to do any of this legwork to begin with, and that's, to me, one of the major selling points of this.
All right, I've uploaded this code to the Devra GitHub account. The code link is in the description, as well as a link to Devra AI so you can get the Devra software and do some analysis like this.

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