Okay, so today I’m gonna walk you through my little project: Hornets prediction. Yeah, you know, trying to guess who’s gonna win Hornets games. Don’t laugh, it was a fun learning experience!
Getting Started: Data, Data, Data!
First things first, I needed data. I spent a good chunk of time scraping game stats from some sports websites. It was a pain, honestly. Lots of inspecting HTML and writing scrappy Python scripts. I ended up with a bunch of CSV files with stuff like points scored, rebounds, assists, you name it. It wasn’t pretty, but it was data!
Cleaning Up the Mess
The data was a mess, naturally. Missing values everywhere, inconsistent formatting, the whole shebang. I used Pandas in Python to clean things up. Filled in missing values with averages, converted data types, that kind of stuff. This part was super tedious, but crucial. Garbage in, garbage out, right?
Feature Engineering: Making Sense of It All
Then came the fun part – trying to figure out what features might actually be useful for predicting wins. I calculated things like average points per game, win percentages, recent performance (like, how they did in the last 5 games). I even tried some more complex stuff like calculating moving averages and using opponent stats. Some of it probably didn’t help at all, but hey, gotta experiment!
Choosing a Model: Keeping It Simple (ish)
I decided to start with a Logistic Regression model. It’s relatively simple to understand and implement. I used scikit-learn in Python for this. Split the data into training and testing sets, trained the model, and then tested it on the held-out data. I also tried a Random Forest model later on, just to see if it would perform better.
Evaluation: How Badly Did I Fail?
The results were… mixed. My initial accuracy was around 60-65%, which is better than a coin flip, but not exactly groundbreaking. I tried tweaking the model, adding more features, and even tried different models (like I said, Random Forest). Nothing really bumped the accuracy up significantly. I’m guessing there’s a lot of randomness in basketball, or maybe I just didn’t have the right data.
Visualizing the Results
I whipped up some simple plots using Matplotlib to visualize the model’s predictions and compare them to the actual results. This helped me see where the model was going wrong and identify patterns. It turns out, predicting close games is really hard!
What I Learned: A Whole Lot!
- Data cleaning is 80% of the work. Seriously.
- Feature engineering can be tricky, but it’s also where you can potentially make the biggest impact.
- Model selection is important, but sometimes the data just isn’t good enough.
- Basketball is unpredictable!
Next Steps: Maybe a More Advanced Model?
If I were to continue this project, I’d probably explore some more advanced machine learning techniques, like neural networks. I’d also try to incorporate more data, like player stats and maybe even some external factors like weather conditions or injuries. But for now, I’m calling it a learning experience. It was a fun way to get my hands dirty with data science and machine learning!
