Okay, so let me walk you through my little side project, the “italy prediction” thing. It’s nothing fancy, just me messing around with some data and trying to see if I could make some educated guesses.
First off, I grabbed the data. I spent a good chunk of time scraping data from various sports websites, you know, the usual suspects for football stats. Cleaned it up, got it into a CSV, the whole shebang. Honestly, this part was the most tedious. Getting the data in the right format is always a pain.
Then came the fun part: the code. I’m a Python guy, so naturally, I pulled out Pandas and scikit-learn. I started with some basic exploratory data analysis (EDA). Looking at win rates, goals scored, goals conceded – all that jazz. Just trying to get a feel for what the data was telling me.
Next up, feature engineering. This is where I tried to get a little creative. I didn’t just want to use the raw stats. I calculated things like goal difference, average goals per game, recent form (wins in the last 5 games), that kind of stuff. The idea was to create features that might be more predictive than the basic numbers.
Now for the model. I started simple, with a Logistic Regression model. It’s easy to understand and quick to train. I split the data into training and testing sets (80/20 split), fit the model to the training data, and then made predictions on the test data.
Of course, the first results were terrible. Like, really bad. But that’s expected, right? So, I started tweaking things. I tried different features, different model parameters, even different models (like Support Vector Machines and Random Forests). I was basically just throwing stuff at the wall to see what stuck.
Eventually, I managed to get the accuracy up to a respectable level. Not perfect, mind you, but good enough for a fun project. I think the key was focusing on the features that seemed to correlate most strongly with the outcome. Also, hyperparameter tuning made a difference.
But here’s the kicker: While the model could predict the outcome of the matches with some accuracy, predicting the score was a whole different ballgame. I tried a few things, like Poisson regression, but nothing really worked that well. It turns out predicting exact scores is much harder than just predicting who will win.
So, what did I learn? Well, data cleaning is a slog. Feature engineering is where the real magic happens. And predicting football scores is surprisingly difficult. But hey, I had fun, I learned a few things, and I now have a slightly better understanding of the beautiful game (and data science).
- Data Collection: Scraped data from sports websites.
- Data Cleaning: Formatted data into CSV.
- EDA: Explored data using Pandas.
- Feature Engineering: Created new predictive features.
- Model Building: Experimented with Logistic Regression, SVM, and Random Forests.
- Model Evaluation: Measured accuracy on test data.
- Score Prediction: Tried Poisson regression, but results were not great.
That’s pretty much the whole story. It was a fun little project, and who knows, maybe I’ll revisit it someday and try to improve the score prediction. But for now, I’m moving on to other things.
