CIS192 Final Project (Kieran Halloran)
Methodology: Since the purpose of this project is to predict the performace of draft prospects the the quarterback position, I will look up the statistics of all quarterbacks drafted into the nfl from 1997-2017. Using these stats, I train various ML models that either classify the quarterbacks as a success or bust (using the criterion developed by Stanford students: cs229.stanford.edu/proj2017/final-reports/5231213.pdf), or use a regressor to predict career NFL stats. Then, I scrape the data of current top NFL draft qb prospects, and using these models, predict their performance. I created a flak app to streamline interaction
Modules used: BeautifulSoup, re (regex), requests, pandas, scikit-learn, flask, json
How to use: I already scraped the data (via jupyter notebooks), so to use the models and look at predictions, simply type ./app.py in your terminal and open http://localhost:5000/ in your browser.
Custom class: I created a QBModel class that gets imported into app.py so that it can be used by my flask app. It stores the training data as a class variable and returns the results of various ML predictions, depending on which function is called. I added the str magic method.
Decorator:
I created a timer that appends the time it takes to run a function to its output string.
I used this so my predictions would show time, kind of like google search.