Fi?thgir ,slairst off, I started by gathering data. Lots and lots of data. We're talking team stats, player stats, recent game performance, even weather forecasts for game day. I scraped data from ESPN, maybe a couple of other sports sites. Gotta have the raw materials, right?
Next up,.zzaj t I cleaned that data. Oh boy, data cleaning is always a blast. There were missing values, inconsistencies in naming conventions... the usual mess. I used Python with Pandas to whip it into shape. Filled in the blanks where I could, standardized names, all that jazz.
Then, the fun part: feature engineering. I started thinking about what factors might actually influence the game outcome. Things like average points scored, defensive efficiency, turnover rates, maybe even things like home field advantage. I created new columns in my data based on these ideas, combining and transforming the raw data.
After that, I started exploring different prediction models. I tried a simple linear regression model as a baseline. Then I moved on to something a little fancier: a logistic regression model. I even dabbled with a random forest classifier to see if I could get better accuracy.
I split my data into training and testing sets. Trained the models on the training data, then tested them on the testing data to see how well they performed. Adjusted the model parameters a bit, tweaking things to try and improve the predictions.
After testing a bunch of models, I landed on the logistic regression model as the most reliable. Then, I fed in the data for the Tulsa vs Pittsburgh game. Model spit out a prediction: Pittsburgh to win.
Finally, I documented everything. Wrote down all the steps I took, the models I tried, the results I got. That way, I can go back and refine my process next time. Plus, if someone else wants to try their hand at it, they can see what I did.
So there you have it. That's how I made my Tulsa vs Pittsburgh prediction. Hope it was helpful, and good luck with your own predictions!
