Alright, let me walk you through my little adventure with the “nets bulls prediction” thing. Honestly, it was more of a “let’s see what happens” kind of project than some grand scheme.
First off, I started by grabbing some data. I mean, you can’t really predict anything without something to base it on, right? I scraped game stats, player info, the usual suspects from a couple of sports websites. Nothing fancy, just good ol’ HTML parsing and a bit of cleaning with Pandas. Let me tell you, cleaning data is like 80% of the job. It’s a pain, but you gotta do it.
Next, I tried a few different models. I’m no AI wizard, so I stuck to stuff I knew – logistic regression, a simple neural network using TensorFlow, and even a random forest. I split the data into training and testing sets, you know, the standard procedure. I fed the training data to the models and tweaked the parameters a bit to see what would happen.
The neural network looked promising at first, but it turned out to be overfitting like crazy. The random forest performed surprisingly well, actually. Logistic regression was decent, but nothing to write home about.
Then, I thought, “What if I combine them?” So, I implemented a simple ensemble method. Basically, I took the predictions from each model and averaged them. It’s like asking a bunch of people for their opinion and then going with the most common one.
Finally, I ran the whole thing on the test data and checked the accuracy. It wasn’t perfect, mind you. We’re talking around 65-70% accuracy, which is better than flipping a coin, but not enough to retire on. But hey, it was a fun exercise. I learned a bit about data manipulation, model selection, and the importance of not trusting everything you see on the internet.
Here’s a quick breakdown:
- Grabbed data.
- Cleaned data (a lot).
- Tried logistic regression, neural network, and random forest.
- Ensembled the models.
- Evaluated the results.
Would I bet my life savings on it? Nah. But was it a good way to spend a weekend? Definitely. And who knows, maybe with some more tweaking and better data, I can actually get something useful out of it.
Things I’d do differently next time:
- Gather more data – the more, the merrier, right?
- Explore more feature engineering – maybe there are some hidden patterns I missed.
- Try more advanced models – maybe it’s time to dive deeper into the AI rabbit hole.
