Today, I wanted to play around with something called “Nakashima Prediction.” Honestly, I didn’t even know what it was until this morning, but I saw a post about it and thought, “Why not?” So, I started digging.
What is Nakashima Prediction?
I hit up Google, and came to know it’s some way to predict failures of disk. Sounds cool, right? Apparently, it uses some math and statistics to figure out when things are likely to go wrong.
Getting My Hands Dirty
First things first, I needed to get some data. There’s some example data out there to help you begin.
I got example data from the Internet.
Once I had the data, I needed to install some required packages.
I tried the command to install:
pip install nakashima-prediction
Seemed to work, no big red error messages, so that was a good start!
Running the Code
Next up, I grabbed some of the sample code I found online. I just copy-pasted it into a new Python file. It looked something like this:
from nakashima_prediction import NakashimaModel
# Load the data
data = #... your data here ...
# Create a model
model = NakashimaModel()
# Fit the model to the data
*(data)
# Make predictions
predictions = *(data)
# Print the predictions. In real life, you'd probably do something with these.
print(predictions)
I replaced `#… your data here …` with my actual CSV data and hit “run”.
The Results (and Confusion)
The code ran, and it spit out a bunch of numbers. Honestly, I’m still not 100% sure what all of them mean. I need to go back and read the documentation a bit more carefully. There were some charts generated too, which looked kind of neat, but again, I need to understand the underlying math better to really interpret them.
Next Steps
Read the docs! I really need to understand the output and the different parameters I can tweak.
Experiment with different data. I want to see how the model behaves with different failure rates.
Maybe try a different library. I saw there are a few other options out there, so I might compare how they perform.
I try to understand what is “shape” and “scale”, and after adjusting the data, I can see different data results. It’s kind of fun.
It was a fun little experiment for the day. I definitely learned something new, even if I’m still a bit fuzzy on the details. That’s the fun of programming, though, right? Always something new to explore!
