Okay, so, let’s dive into this collins maia prediction thing. It’s been a bit of a ride, let me tell ya.
It all started when I stumbled across this article talking about some new prediction model. My brain just went, “Hey, that looks kinda cool, let’s mess with it!” So, first thing I did was hunt down the original paper. You know, gotta see if it’s legit or just some fancy marketing fluff. Turns out, the math looked solid, at least to my untrained eyes.
Next up, trying to find some code. Ideally, I wanted something in Python, cause that’s what I’m most comfortable hacking around in. Lucky for me, someone had already put up a basic implementation on GitHub. Score! I grabbed that sucker, and started poking around.
The initial code was pretty barebones. It worked, kinda, but it wasn’t exactly plug-and-play. So, I started cleaning it up. First, I added some better error handling. You know, making sure it doesn’t just explode if you feed it garbage data. Then, I messed with the input format. It was expecting some weird CSV thing, and I wanted to use something more standard, like JSON. That took a bit of wrangling with the pandas library, but got there in the end.
Then came the fun part – actually trying to use it. The paper talked about predicting stock prices, so I figured I’d give that a whirl. I downloaded some historical stock data from Yahoo Finance (bless those guys). Then I fed it into the model. The results… well, they were interesting. Let’s just say I’m not quitting my day job to become a stock trader anytime soon. It was predicting all sorts of crazy fluctuations that just weren’t happening in real life.
So, back to the drawing board. I started digging into the model’s parameters. There were a bunch of hyperparameters that I could tweak. I tried a bunch of different combinations, using a grid search approach. Basically, just systematically trying out all the possible values. That took FOREVER to run. And the results were… marginally better. Still not great.
I started to wonder if maybe the stock market was just too noisy of a dataset for this model to work well. So, I looked around for some other datasets that were used in the original paper. Turns out, they had some success predicting customer churn. So, I found a public dataset of customer churn information and tried it out. And… it actually worked! Not perfectly, mind you, but it was definitely better than random guessing.
I think the key takeaway here is that these prediction models are super sensitive to the data you feed them. Garbage in, garbage out, as they say. And even with good data, you still need to do a lot of tweaking and tuning to get good results.
Here’s what I ended up doing:
- Grabbed the original code from GitHub.
- Cleaned up the code and improved the error handling.
- Changed input format to JSON
- Downloaded stock data from Yahoo Finance and tested on stock prices
- Tweaked the model parameters
- Found a customer churn dataset and tested it with this dataset instead.
It wasn’t easy, but I learned a lot about how these models work (and don’t work). And who knows, maybe one day I’ll actually be able to predict the future. But for now, I’m just happy I didn’t blow up my computer in the process.
