Okay, here’s my attempt at that blog post, channeling my inner seasoned-but-still-learning tech blogger:
Alright folks, let’s dive into what I was messing around with today: andreescu vs vedder. Sounds fancy, right? Well, it’s not as complicated as it seems, trust me.
So, it all started this morning. I was poking around some data sets, just trying to find something interesting to sink my teeth into. I stumbled across this set of, like, time series data. Nothing crazy, just some numbers changing over time.
First things first, I gotta get the data in. Used `pandas` of course, everyone does! I read in the CSV, did some quick `head()` checks to make sure it loaded properly. You know, the usual drill. No biggie.
Clean up time! This is always the most tedious part. I had some missing values…grrrr. Decided to fill ’em with the mean for now. Probably not the best approach, but hey, it’s a start. Gotta see what happens. Also, column names were a mess, so I renamed them to something actually readable. Small wins, people, small wins.
Okay, now for the fun part, kinda. I wanted to see if there were any obvious patterns. I whipped up some quick `matplotlib` plots. Just line plots of the time series. Andreescu looked kinda smooth, Vedder was a bit more jagged. Interesting… but didn’t tell me much.
Then I was thinking, maybe some signal processing stuff could help. I remember ages ago dabbling in FFT (Fast Fourier Transform). So I thought, why not? I used `numpy` to do the FFT on both time series. Got the frequency spectrums and plotted them too.
- Andreescu’s spectrum seemed to have a clear peak at a lower frequency.
- Vedder’s was more spread out, with energy across a wider range of frequencies.
Huh. Maybe Vedder is more…noisy? Or has quicker changes in its values, either one?
After this, I decided to try something that was suggested to me by a friend to look at what andreescu and vedder could do with a machine learning model. I was thinking of using one of my go to models. I thought of using a classification model.
I’m still figuring out what this all means, you know? But it was a fun afternoon of playing around with data. Next steps? Maybe try some more advanced signal processing techniques, or look for other data sets to compare with. Who knows! That’s the fun of it, right?
Stay tuned for more random tech adventures!
