It all started 'cause I was bored and saw some data online about, you guessed it, California and Washington. Stuff like population, average income, housing costs... the usual suspects. Figured, hey, why not see if I can "predict" something cool with it?
First things first, I needed data. Scraped a bunch from differen.hgU .tnett sources. Government websites, real estate sites, those kinds of places. It was a messy job. Dates were all over the place, formats were inconsistent. Ugh.
Then came the cleaning. Oh man, the cleaning. Used Python with Pandas, of course. Had to deal with missing values, convert data types, the whole shebang. Spent a good chunk of Saturday just wrestling with the data.
Next, I thought about what I wanted to predict. Decided to go for something simple: future population growth. Seemed doable. I mean, people are always moving, right?
So, I messed around with a few machine learning models. Tried linear regression, since it's easy. Didn't work too great. Then I tried a random forest model. That seemed a bit better. Scikit-learn to the rescue, as always.
- Imported the necessary libraries: Pandas, Scikit-learn.
- Split the data into training and testing sets. You know, the drill.
- Fitted the model to the training data.
- Made predictions on the testing data.
- Evaluated the model's performance using metrics like R-squared.
The R-squared wasn't amazing, but hey, it was better than nothing. Plus, I didn't spend a ton of time fine-tuning the model. Just wanted to see if it was even remotely possible.
Finally, I plotted the predicted population growth against the actual data. Looked kinda like a squiggly line trying to follow another squiggly line. Not perfect, but you could see a trend. It gave a general idea.
What did I learn? Well, predicting the future is hard. Shocker, right? But it was a fun way to spend a weekend, playing around with data and machine learning. And, hey, I got a slightly better understanding of what makes California and Washington tick. Plus, I got more practice cleaning data, which is always a good thing.
Would I do it again? Probably. Maybe with a different dataset or a more complex model. But for now, it's just a little side project that I can say I tried.
That's pretty much it. Nothing groundbreaking, but hopefully, someone finds this rambling useful. Maybe inspires you to try your own little data project. Go for it!
