Okay, so yesterday I was messing around with some data I pulled from a game, trying to see if I could predict, like, whether a “sinner” or a “rune” would show up next. Sounds kinda dumb, I know, but hear me out.
First, I grabbed a bunch of game logs. Like, a ton of them. We’re talking weeks of playtime data, all nicely dumped into text files. Then I had to wrangle that mess into something usable. I’m no data scientist, so this was basically me googling Python snippets and hoping for the best.
I started by parsing the logs. Basically, I wrote a script that would go through each line and look for the keywords “Sinner appeared” or “Rune spawned.” When it found one, it would record the timestamp and the type of event. Pretty straightforward.
Then came the fun part: trying to find patterns. I figured maybe there was some kind of sequence, like, “Sinner, Rune, Rune, Sinner” repeating over and over. So I tried a bunch of different things. First, I just looked at the raw sequence of events. No dice. It was completely random, or at least seemed like it.
Next, I tried looking at the time between events. Maybe Sinners always spawned, like, 5 minutes after a Rune? I wrote some code to calculate the time differences and plotted them on a graph. Nope. Still random. Big surprise.
I even tried some more complicated stuff, like using a Markov chain to predict the next event based on the previous few. That actually gave me slightly better results, but it was still only, like, 55% accurate. Not exactly a winning strategy.
Here’s what I ended up doing that got me the most interesting results, which really wasn’t all that great:
- Cleaning the Data: Before anything else, I removed duplicate entries and any corrupted data points to ensure accuracy.
- Feature Extraction: I identified relevant features such as:
- Time of Day: Dividing the day into segments (morning, afternoon, evening, night).
- Day of Week: Differentiating between weekdays and weekends.
- Previous Event: The type of the immediately preceding event (Sinner or Rune).
- Time Since Last Event: The duration since the last spawn of either type.
- Model Selection: I experimented with several classification algorithms:
- Logistic Regression
- Random Forest
- Gradient Boosting (e.g., XGBoost, LightGBM)
- Training and Validation:
- I split the dataset into training (80%) and validation (20%) sets.
- I used cross-validation on the training set to fine-tune the model parameters.
- I evaluated the model’s performance using metrics like accuracy, precision, recall, and F1-score.
- Feature Importance Analysis: I used feature importance metrics (available in models like Random Forest and XGBoost) to understand which features had the most significant impact on the prediction.
So, yeah, that’s pretty much it. I didn’t exactly crack the code, but it was a fun little project. Maybe I’ll try again with some different data or a more sophisticated model. Who knows, maybe one day I’ll be able to predict those spawns with 90% accuracy. A guy can dream, right?
