Okay, here’s my attempt at a blog post reflecting my hands-on experience, written in a casual and relatable tone, just like you asked!
## My Gojo Prediction Journey – Let’s Break it Down!
Alright, so I was messing around with some data stuff the other day, and the idea of predicting stuff about Gojo Satoru from Jujutsu Kaisen popped into my head. Why? Because why not! Seemed like a fun challenge, and a good way to actually put some of the stuff I’ve been learning into practice.
First off, I needed data.
This was the initial hurdle. I spent hours digging around forums, wikis, and even re-watching episodes, scraping together any kind of info I could get my hands on. Things like his fight records, the types of curses he’s dealt with, even details about his personality from character bios. It was a messy process, copying and pasting stuff into a spreadsheet. Not pretty, but it was a start.
- Gathered data from various Jujutsu Kaisen resources
- Consolidated everything into a CSV file
- Started feeling like a crazy person talking about anime characters
Next up, cleaning the chaos.
The data I had was, to put it mildly, a garbage fire. Different formats, missing info, and just plain inconsistencies everywhere. I fired up Python with Pandas, because that’s what all the cool kids are doing and started scrubbing. Filling in missing values with averages, standardizing the text, and generally making the data usable. This part was tedious, but super important. Garbage in, garbage out, right?
Time to pick a direction.
I realized I couldn’t predict everything about Gojo all at once. So, I narrowed my focus to predicting his win rate in future battles, based on factors like the opponent’s strength and the type of curse they wield. It’s a pretty specific goal.
Model selection – the fun (and confusing) part.
I played around with a couple of different machine learning models. I initially tried a simple logistic regression, thinking it might be a good baseline. But it was too basic and didn’t really capture the nuances of the data. Then I experimented with a Random Forest model, and that seemed to give me better results. After some tweaking, I settled on that. I’m no expert.
- Experimented with Logistic Regression and Random Forest
- Settled on Random Forest for its better performance
- Spent way too long Googling error messages
Training and testing.
I split my data into training and testing sets. Trained the model on the training data, and then used the test data to see how well it performed. I used metrics like accuracy and precision to evaluate its performance. Turns out, my model wasn’t perfect (shocker!). It was able to predict Gojo’s win rate with about 75% accuracy. Not bad for a first attempt.
What I learned from all this?
Data is messy, data cleaning is a pain, and machine learning is hard but super interesting. While my Gojo prediction model is far from perfect, it was a really valuable experience. I got to practice my data wrangling skills, experiment with different machine learning models, and learn a ton along the way. Plus, I now have a slightly better (and probably completely useless) understanding of Gojo’s fighting prowess.
Next steps.
I want to gather more data, refine my model, and maybe even try to predict other aspects of the Jujutsu Kaisen universe. Who knows, maybe one day I’ll be able to accurately predict the next big plot twist!
Anyway, that’s my journey so far. It’s a bit rough around the edges, but hopefully, it gives you a glimpse into what it’s like to try and apply data science to something completely random and fun.
