Okay, here’s my take on sharing my experience with “metz open,” just like a seasoned blogger would.
Alright folks, lemme tell ya about this “metz open” thing I tackled. So, it all started when…
First things first, I downloaded the dataset. It was a hefty one, took a good chunk of time. Then, I spent a while just exploring it. You know, poking around, seeing what kind of data we’re talking about. Opened it up in Pandas, you gotta see the structure, the columns, the whole shebang.
Next up, cleaning. Oh man, the cleaning. There were missing values all over the place! Decided to impute them with the mean for the numerical columns. Figured that was a safe bet to start with. For the categorical ones, I just filled them in with the most frequent value. Quick and dirty, but hey, gotta move fast.
Now comes the fun part: feature engineering. I tried a few things here. Created some interaction terms between some of the features that I thought might be related. Also, did some scaling – Standard Scaler, to be precise. You gotta bring those features onto a similar scale, right? Tried some polynomial features too, just to see if anything interesting popped up. It did not.
Then, the models! I started with something simple: a Logistic Regression. Just to get a baseline. Then I moved onto a Random Forest. That performed a bit better. After that, I tried an XGBoost. Now we’re talking! That one was the best so far.
But I wasn’t satisfied. I wanted to tune those hyperparameters. So I used Grid Search, but the search space was kinda big, so it took a while to run. A long while. While that was running I thought I would try something different, so I threw in a Support Vector Machine(SVM) to see if it could give a better result, this took ages. Turns out it wasn’t great!
After a few more runs, I got some decent parameters for the XGBoost. The validation score improved, but not by a crazy amount. Small improvement is still improvement. I did some fine tuning using some old tricks I know.
Finally, I made the predictions on the test set and submitted them. Fingers crossed! The score wasn’t amazing, but it was respectable. Definitely learned a bunch along the way.
Here’s a list of things I would do differently next time:
- Spend more time on feature engineering. There’s probably some hidden gems in that data that I missed.
- Try some more advanced models. Maybe a neural network?
- Be more careful with the data cleaning. Double check the NaN values and the most obvious outliers.
So yeah, that was my experience with “metz open”. It was a grind, but hey, that’s how you learn, right? Hope this helps someone out there!
