Okay, so today I’m gonna walk you through this little project I tackled – trying to build something around a “tennis player spanish” theme. Sounds kinda random, right? Well, that’s usually how my best experiments start.
First off, I did the usual Google dive. I wanted to get a feel for what’s out there, popular Spanish tennis players, any interesting data sets, that kind of thing. I spent a good hour just soaking up info, making notes on anything that sparked an idea.
Next, I started thinking about what I actually wanted to do. Did I want to build a simple website showcasing players? Maybe a little quiz? I bounced around a few ideas, finally settling on trying to create something that could predict a player’s ranking based on some stats.
Alright, data gathering time. This was the most tedious part, honestly. I scraped data from various tennis websites – ATP rankings, match results, win percentages, you name it. Spent a solid afternoon cleaning it all up in a spreadsheet, removing inconsistencies, and making sure it was actually usable. Ugh.
Once I had my data somewhat organized, I started messing around with some simple machine learning models. I’m no expert, but I’ve played around with Python and scikit-learn before. I trained a basic linear regression model to see if I could get any kind of correlation between stats and ranking. The results weren’t amazing, but it was a start.
I realized pretty quickly that just throwing all the data into a linear regression wasn’t gonna cut it. So I started thinking about feature engineering – trying to create new, more meaningful data points from the existing ones. Things like “win percentage against top 10 players,” “average number of aces per match,” that sort of thing.
After some tweaking and experimenting with different models (tried a random forest and a support vector machine too), I managed to get something that was… somewhat decent. Still not perfect, but it could at least predict a player’s ranking within a reasonable range.
The last step was just to throw a quick front-end on it so I could actually play around with it. I used a simple HTML/CSS setup and some JavaScript to feed data into the model and display the predicted ranking. It’s ugly, but it works.
Learnings:
- Data cleaning is always more time-consuming than you think.
- Feature engineering can make a huge difference in model performance.
- My machine learning skills are still pretty basic, but I’m learning!
Next steps:
- Get more data! The more data, the better the model will be.
- Explore more advanced machine learning techniques.
- Actually make the front-end look presentable.
So yeah, that’s my “tennis player spanish” project in a nutshell. It was a fun little experiment, and I learned a lot along the way. Maybe I’ll revisit it later and actually turn it into something useful.
