Alright, folks, let’s dive into this “Coventry prediction” thing I’ve been messing with. I gotta say, it started as a total shot in the dark, but it’s turned into something pretty interesting.
It all began when I stumbled upon some random article about predicting… well, something. I can’t even remember what it was now. But it got me thinking, could I predict something about Coventry, my local city? I mean, why not, right?
First, I needed data. Lots of it. So, I started scraping websites. I know, I know, not the most glamorous part, but hey, gotta get your hands dirty. I pulled info from the local council website, news sites, even some weather pages. It was a mess, to be honest. A big, jumbled pile of text and numbers.
Next up, I needed to make sense of this mess. I used some basic Python scripts – nothing fancy, just stuff I cobbled together from online tutorials. (I’m no coding whiz, believe me!) I cleaned up the data, removing duplicates and irrelevant bits, formatting dates, and basically trying to wrangle it into something usable.
Then came the “prediction” part. I wasn’t aiming for anything super specific, just wanted to see if I could find any patterns. I started with something simple: trying to see if there was a correlation between the weather and the number of events happening in the city.
- Pulled weather data (temperature, rainfall, etc.).
- Grabbed a list of events from the council website (festivals, markets, etc.).
- Compared Date.
To do this I try to create simple chart to compare the relationship between those data.
Initially the result is not I expected, total mess and no solid pattern. But I’m not giving up that fast!
I dig deeper, and realize there are some “big” event that make the data “not normal”, So I try to take the data on a normal day, or on event with less audience.
And Bam! Finally I see it, the pattern!, at some range temperature and sunny day, there are slightly increase on some event.
The “Aha!” Moment
It is not much, but It’s work!. I mean, it wasn’t groundbreaking, but it was something. It showed that there was at least a tiny bit of predictability in the data. I felt like a detective who’d just cracked a tiny, insignificant case. But a case nonetheless!
So, that’s my Coventry prediction adventure so far. It’s been a bit of a rollercoaster, with lots of trial and error, but it’s been fun. I’ve learned a ton, and who knows, maybe I’ll even refine it into something more useful down the line. For now, it’s just a fun little project that proves even a coding newbie like me can find some interesting patterns in the world around us.
