Okay, so today I decided to mess around with this “gabriel diallo prediction” thing. I’d seen some buzz about it online, and, well, I’m a sucker for trying out new prediction models. I’m no expert, but I like to tinker.
First, I had to figure out what I even needed. I spent some time digging around, mostly just Googling stuff. I realized pretty quickly that I needed some kind of environment set up. It seemed like Python was the way to go, since most of these things are built with it.
So, I fired up my trusty old laptop. It’s not the fastest thing, but it gets the job done. I already had Python installed (thank goodness!), but I know I needed some extra libraries. I think they’re called…packages? Yeah, packages. Stuff like… I think it was called ‘pandas’ for handling data, and probably something for the actual prediction model itself. I was not totally sure yet.
I then created a new project folder. I just called it ‘diallo-prediction-test’ – super original, I know. Inside, I created a new Python file, ‘*’. This is where I planned to write all my code.
Next up, the libraries! I used ‘pip’, that thing that comes with Python, to install them. I basically typed ‘pip install’ followed by the name of each library I thought I needed into the terminal. I hit enter, and crossed my fingers. It seemed to work! A bunch of text scrolled by, and no scary red error messages popped up.
I have to prepared something called ‘*’ and put the names of the needed packages inside. I did:
pip freeze > *
Now came the hard part: actually finding some code examples. I’m not smart enough to write this stuff from scratch! I went back to Google, searching for things like “gabriel diallo prediction example” or “gabriel diallo prediction python”.
I found some snippets here and there, mostly on forums and in blog posts. Some of it looked like gibberish, but I copied and pasted the parts that seemed relevant into my ‘*’ file. I spent a good chunk of time just trying to understand what the heck I was even looking at. There was a lot of stuff about loading data, cleaning data, training a model… it was a lot.
After some trial and error (mostly error!), I managed to get something that ran. I mean, it didn’t crash immediately, which felt like a victory. It spit out some numbers, which I think were supposed to be predictions. I have no idea if they were accurate or not, but hey, it was something.
Finally, I added some simple ‘print’ statements to my code to show the results in a slightly more readable way. I’m not a big fan of fancy charts and graphs, so I just printed the raw numbers to the console. Good enough for me!
I have to said it’s not perfect. I’m sure a real data scientist would laugh at my messy code and probably inaccurate results. But, I learned a little bit, and I had some fun doing it. That’s what matters, right? Maybe next time, I’ll try to actually understand what’s going on under the hood, instead of just blindly copying and pasting. But for now, I’m calling it a day!
