Sharon Machlis
I requested for visualizations, and it responded:
There are numerous attention-grabbing variables we might visualize. Let me counsel just a few prospects:
Steered subsequent steps:
Are you able to create a histogram of sport complete scores to see the distribution of scoring?
Might you make a field plot evaluating house vs away crew scores?
Let's create a scatter plot of temperature vs complete rating to see if climate impacts scoring.
Are you able to present me the distribution of betting spreads and the way they relate to precise sport outcomes?
Might you create a visualization displaying win/loss data by crew?
Which of those pursuits you, or would you wish to discover one thing else within the information? I will help create any kind of visualization you'd discover helpful!I requested for the climate versus scoring plot, and it wrote R code that sadly didn’t present an attention-grabbing pattern. Subsequent, I attempted the histogram of complete scoring, which returned R code, the bot’s observations, and a plot. Databot then supplied the next evaluation:
This visualization reveals a number of attention-grabbing patterns about NFL scoring:
1. The distribution seems roughly regular (bell-shaped), with some slight proper skew
2. The imply complete rating is about 44.5 factors
3. The median could be very near the imply at about 44 factors
4. Most video games fall between roughly 30 and 60 complete factors
5. There are just a few outlier video games with very excessive scoring (80+ factors) and really low scoring (
Sharon Machlis
Whereas many mainstream chatbots have comparable capabilities, few write and run R code like this; generative AI information evaluation code is sometimes accomplished in Python.
