Okay, here’s my sharing about the “gsw bulls” practice, mimicking the style you requested.
Alright folks, let me tell you about this thing I was messing with – I called it “gsw bulls” just ’cause I was listening to sports radio at the time, Golden State Warriors and Chicago Bulls were playing, nothing deep.
So, I started out with this dataset, right? Found it on Kaggle, pretty standard stuff, player stats, game outcomes, that kinda thing. First thing I did was load it up in Pandas, because that’s what you do, ain’t it? Then I took a look, you know, `*()`, `*()`, the usual. Gotta see what you’re working with.
Then I decided, hey, I want to predict whether a team is gonna win or lose. Binary classification, easy peasy. So I started cleaning the data. Missing values? Yeah, there were a few. Filled ’em in with the mean for now, didn’t want to overthink it.
Next up, feature engineering. Now, I’m no expert, but I figured some stats are more important than others. I calculated a few things like win percentage, points differential, stuff like that. Basically, trying to create features that a simple model could learn from.
After that, I split the data into training and testing sets. You gotta do that, right? 80/20 split, used `train_test_split` from Scikit-learn. Then, I scaled the features using `StandardScaler`. Don’t want any features dominating just because they have bigger numbers.
For the model, I went with a simple Logistic Regression. I know, not fancy, but hey, gotta start somewhere. I trained it up on the training data using `.fit()`, and then I predicted on the testing data using `.predict()`.
Then, I checked out the results. Accuracy score, confusion matrix, the whole shebang. The accuracy wasn’t amazing, like 70%, but hey, it’s a start. I think more feature engineering or a different model could definitely improve it.
I spent some time trying out different models, like a Random Forest and a Gradient Boosting Classifier. Random Forest gave me slightly better results, maybe around 75% accuracy. I didn’t bother with hyperparameter tuning too much, was just messing around.
Finally, I saved the model using `pickle`, so I could load it up later without having to retrain it. That’s pretty much it. Nothing groundbreaking, but I learned a few things along the way.
- Data cleaning is key. Garbage in, garbage out, as they say.
- Feature engineering can make a big difference. Creating the right features can really help the model learn.
- Simple models can be surprisingly effective. Don’t always need to go straight for the fancy stuff.
Next Steps?
If I were to take this further, I’d probably try:
- More sophisticated feature engineering (e.g., interactions between features).
- Hyperparameter tuning for the Random Forest model.
- Trying a different model architecture, maybe a neural network.
- Getting more data! More data is always good.
So yeah, that’s the “gsw bulls” project in a nutshell. Just a little data science fun. Hope you enjoyed hearing about it!
