Predict NFL Touchdowns - Create Your First Predictive Model in Python (Step by Step Tutorial)

[@tweakyapollo]

I am a 35 year old intern starting a career in analytics. Thank you for this

[@Rapha_Carpio]

36 y/old here

[@ak22196]

@@Rapha_Carpio hey good luck to you both. How did you find internships are older appliers?

[@Aj_J132]

I found this tutorial very helpful and really helped a beginner/student like myself. Keep it up!

[@daniellepashayan773]

@4:01 - create a list of the seasons you want to include and do a nested list comprehension using any. this helps too so if you ever want to add more seasons, you just edit the variable value instead of changing the code in the list comp desired_seasons = ['2019', '2020', '2021'] target_seasons = [season for season in seasons if any(target in season for target in desired_seasons )]

[@WingfanSports]

Great video. For anyone that cares Matt Stafford no only switched teams but also played in the super bowl that year giving him 4 additional games. This model would be significantly better if you limited it to regular season only and maybe strip out players who switched teams

[@peterk6215]

Thanks Nick. This was great! Hope that you do more in the future. 🙂

[@WingfanSports]

This was Herbert second season so a big jump not surprising. Adding some sort of age curve to this might also be smart

[@Rapha_Carpio]

13:56 maybe you can get rid of all nan values.... just dropping it or fill those NaN with the mean..

[@briankkrieger1685]

I'm a complete noob, but I'm trying to learn. Do you have a video or instructions on how you download the github data so you can use it when following along in google collab

[@h00die49]

Love this type of content, thanks Nick!

[@Rapha_Carpio]

25:42 Can we like put colors to the top 5 Preds so if we are like looking to "buy" a new quarterback we can go for the top 5... I don't think you need all the 20 seasons, usually a player play at the top level 5-7 seasons..

[@longieboy]

Very nice! Could you bring more videos on prediction sport outcomes with python?

[@Rapha_Carpio]

15:02 Previous interceptions has nothing to do with the "next year touchdowns" because, the interceptions might be fault of the offensive player that did not catch the ball... or did not read the play very well and the defend just got the ball on the go... for newbies... we see that the correlation is so weak in that graphs "touchdowns and interceptions_prev" the line blue is not so high.

[@michaelg9359]

how do you get the data?

[@jeremyespinoza279]

Could you make a NFL fantasy one.

[@tyler_russell]

If you have more data, why wouldn’t you train with it? For example, why not 2019 and 2020 to train and then test on 2021.

[@colinbehan9330]

It’s a shame you don’t respond to anyone in here asking questions for support. Not a good strategy for building a following!

[@My_oh_face]

yeah for that reason im outta here

[@My_oh_face]

if anybody knows a content creator that responds to messages let me know

[@averagejoe5822]

Nice! Love the content

[@briankkrieger1685]

Also is there a resource for you to learn what pandas, matplotlib, and seaborn do for you?

[@smasher7144]

I'm having some trouble with the loop and append section is there any other documentation to follow? Using VSCode for reference

[@zachwells2842]

Same

[@storiebrand4682]

Explained very well thanks bro

[@tweakyapollo]

Does this only work with csv? Or can this be an Excel sheet

[@hastyscorpion]

You can export a excel file as a csv

[@HoofSC]

szns = ['2019', '2020', '2021'] target_seasons = [x for x in szn_folders if x in szns]

[@Hurtchie]

did it work?

[@MoanhLG]

wtf, you just only explain the code with the command. IDK what can I get from your video ?

[@1o11er]

Dank

[@jwild8293]

Boring as hell. 5 Stars.

[@rotondwasithagu2210]

Nonsense