Alright folks, let me tell you about this “utah cal prediction” thing I was messing around with recently. It was a bit of a rabbit hole, but hey, that’s half the fun, right?
So, I started by thinking, “Okay, gotta get some data.” Found some datasets online – you know, housing prices in Utah, some California data too. Cleaned ’em up a bit, ’cause they were kinda messy. Missing values all over the place, different formats…the usual headache.
Next up, I figured, “Gotta pick a model.” Considered a bunch of stuff – linear regression seemed like a good starting point, then maybe something fancier like a random forest. Ended up going with a gradient boosting machine (GBM) ’cause I heard it’s pretty good with these kinds of datasets. Plus, I wanted to try something new.
Now, this is where it got interesting. I split the data into training and testing sets. Used the training set to, well, train the GBM model. Messed around with the hyperparameters a bunch – learning rate, number of estimators, max depth, you name it. It was a lot of trial and error, just tweaking things until the model started behaving itself.
Then came the moment of truth: testing the model. Threw the test data at it and saw what it spat out. The results were…okay. Not amazing, not terrible. Definitely some room for improvement. I started digging into the errors, seeing where the model was messing up the most.
One thing I noticed was that location seemed to be a big factor. Like, houses in certain areas were consistently being over- or under-predicted. So, I tried adding in some extra features – maybe some geographic data, like distance to the nearest city center, or school ratings. That helped a bit, but not as much as I’d hoped.
I even tried some feature engineering – creating new features based on combinations of existing ones. Like, maybe the ratio of square footage to number of bedrooms, or something like that. Some of it worked, some of it didn’t. It was all about experimenting and seeing what stuck.
Eventually, I got the model to a point where I was reasonably happy with the performance. It wasn’t perfect, but it was good enough for a first pass. I even deployed it as a simple web app so I could play around with it and get some real-world feedback.
Here’s the thing: this whole process was a lot more iterative than I expected. It wasn’t just about picking a model and training it. It was about constantly evaluating, tweaking, and refining. Data cleaning, feature engineering, hyperparameter tuning – it all adds up.
Key Takeaways:
- Data cleaning is crucial. Garbage in, garbage out, as they say.
- Feature engineering can make a big difference. Think creatively about how to represent the data.
- Don’t be afraid to experiment with different models and hyperparameters. There’s no one-size-fits-all solution.
- Evaluate your model’s performance and identify areas for improvement. Look at the errors and try to understand why they’re happening.
So yeah, that’s my “utah cal prediction” adventure in a nutshell. It was a fun little project, and I learned a ton along the way. Now, onto the next challenge!
