Alright folks, lemme tell you about this “nice prediction” thing I’ve been tinkering with. It’s not perfect, but hey, it’s my journey, right?
So, I started with this idea, seeing if I could kinda predict stuff based on, you know, data. Nothing fancy, just wanted to see if I could make something work. I grabbed a dataset – some sales data I found lying around. Looked kinda clean, so I thought, why not?
First thing I did was clean that data. Man, data cleaning is a pain. Missing values, weird formats, the whole shebang. I used Python, cause that’s what I’m comfortable with, and pandas. Pandas is a lifesaver, seriously. Filled in the missing bits with averages, standardized the formats, the usual drill.
Next up, I picked a model. I went with a simple linear regression. Figured it’s a good starting point. I used scikit-learn for that. Split the data into training and testing sets. 80/20 split, seemed fair. Trained the model on the training data. That part was actually kinda quick.
Then came the fun part: testing! I threw the test data at the model and watched what happened. The results? Well, let’s just say they weren’t exactly mind-blowing. The predictions were…off. Like, way off in some cases. But hey, that’s why we test, right?
I started digging into why things weren’t working. Turns out, the data had some patterns I didn’t account for. There were seasonal spikes and dips that linear regression just couldn’t handle. So, back to the drawing board.
I decided to try something different: a simple moving average. Basically, averaging out the sales data over a certain period to smooth out the fluctuations. It’s a super basic technique, but I wanted to see if it would help.
I implemented the moving average in Python, played around with the window size (the number of data points to average), and ran it on the test data. And… it was better! Not perfect, but definitely better. The predictions were closer to the actual values, and the seasonal spikes were smoothed out nicely.
I spent some time tweaking the window size to get the best possible results. It was a lot of trial and error, but eventually, I found a window size that seemed to work pretty well. The predictions weren’t spot-on, but they were good enough for a basic proof of concept.
Now, I’m thinking about trying some more advanced models. Maybe a time series model like ARIMA or something with neural networks. But for now, I’m pretty happy with what I’ve got. It’s a simple prediction model that actually works, and I learned a ton along the way.
So yeah, that’s my “nice prediction” journey so far. It’s a work in progress, but I’m excited to see where it goes. Stay tuned for more updates, and let me know if you have any tips or suggestions!
