Okay, so today I decided to mess around with something called “freiburg prediction.” I’d heard about it a little, and it sounded kinda interesting, like trying to guess the future, but for, you know, boring stuff, not lottery numbers.
Getting Started
First, I had to figure out what I even needed. Turns out, there’s this thing called a “dataset.” It’s basically a giant pile of information, like a spreadsheet, but way bigger and messier. I found one online that had all this data about Freiburg, like weather patterns, maybe traffic info, I don’t even know, it was a lot.
Cleaning up the Mess
The dataset, though, was a total disaster. It had missing bits, weird numbers that didn’t make sense, and some stuff was just plain wrong. So, I spent a good chunk of time “cleaning” it. This involved a lot of Googling, I’m not gonna lie. I had to figure out how to fill in those missing gaps, how to deal with the crazy outliers, and basically make it so a computer could actually understand it.
- Finding missing values: This was like playing detective, trying to figure out what should have been there.
- Dealing with outliers: Some numbers were just way off, like someone typed in 999 degrees for the temperature. Had to get rid of those.
- Formatting everything: Computers are picky, so I had to make sure everything was in the right format, like dates and numbers and stuff.
Building the ‘Brain’
After all that cleaning (which took forever, seriously), I could finally start building the “prediction” part. This is where it got a little complicated. I used some pre-built tools, some “libraries,” to help me out. I didn’t write all the code from scratch, because, who has time for that?
Basically, I fed the cleaned-up data into this “model.” It’s like a little brain that learns from the data. I showed it the past, and it tried to figure out the patterns, like “if it was sunny yesterday, it’s probably sunny today” kind of stuff, but way more complex.
Testing and Tweaking
Once the “brain” was trained, I had to test it. I gave it some data it hadn’t seen before and asked it to predict what would happen. At first, it was pretty bad. Like, really bad. It was basically guessing randomly.
So, I went back and “tweaked” things. I changed some settings, adjusted some parameters (fancy words for “knobs” you can turn), and tried different models. It was a lot of trial and error, a lot of waiting for the computer to do its thing, and a lot of staring at graphs that looked like squiggly lines.
The Result (Sort Of)
Eventually, I got it to a point where it was… okay. It wasn’t amazing, but it was definitely better than random guessing. It could predict some things with a reasonable degree of accuracy. I wouldn’t bet my life on it, but it was a fun experiment.
It showed me how much work goes into this kind of stuff. It’s not magic, it’s just a lot of data, a lot of cleaning, and a lot of tweaking. And honestly, I learned a ton just by messing around. I might even try it again with a different dataset sometime, maybe something about predicting… I don’t know, pizza sales? Who knows!
