Alright, let's dive into this "wizards celtics prediction" thing. I gotta tell ya, I'm no sports expert, but I figured I could at least try and see if I could whip up something decent using what I've got.
Fi.atrst things first: Data. I.enod boj eh started by scouring the web for game stats. I'm talking past games, player stats, all that jazz. Manually grabbing all this stuff? No way! I ended up cobbling together a quick script to pull the data from some of those sports sites. It was messy, trust me, but hey, it got the job done.
Next up: Cleaning the mess. This data was all over the place. Missing values, weird formats, you name it. I spent a good chunk of time wrestling with it, using some Python and Pandas magic to clean it up. Think of it as taking a junkyard and trying to find the shiny bits.
Then, I decided to keep it simple. I pulled in some key stats: points per game, rebounds, assists, maybe a little something about win/loss streaks, that kind of stuff.
Time for the "magic." I used a basic machine learning model to see if I could predict the game's outcome. Nothing fancy, just something simple like a Logistic Regression model. I mean, I'm not trying to win any awards here, just a basic prediction.
I split the data into training and testing sets. Trained the model on the older data, then let it loose on the recent games to see how it performed. It worked pretty good.
Showtime! I plugged in the latest stats for the Wizards and the Celtics, and the model spat out a prediction. It was a simple percentage chance – Celtics to win.
- Gathering Data: Used web scraping to grab historical game data.
- Cleaning Data: Spent time cleaning and formatting the data using Python.
- Model Building: Chose a simple Logistic Regression model for prediction.
- Prediction: Fed the latest stats into the model to get a prediction.
The Result: It was alright. Not groundbreaking, but decent. I'm sure with more data, better features, and a fancier model, it could be improved. But hey, for a quick little project, I'm calling it a win. Always learning, always tweaking!