Predicting the Winning Team with Machine Learning

Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn machine learning library as our predictive tool.

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[saugatapaul]

Thumbs up if you guys want a part 2 of this video (using deep learning + twitter sentiments)!

[naminorman]

I just started learning python and this channel is great to see what I can do with the knowledge! Keep up the great work Siraj!

[tizahex]

This topic is awesome!! And it's a good example on what you can do with ML! :) Nice job!

[bxelbjp]

I'm an I.T. Architect, not a Data Scientist. You have no idea how quickly I am learning what I need to from you after jumping into this field. Keep up the amazing work. This isn't easy stuff, but I love it. Thank you for all you do.

[FilipSinjer]

Thanks so much for this video - learned more from this than any other football / modeling video on youtube. cheers!

[j.sch.]

thank you so much for posting such marvelous quality content out there on youtube so frequently! i'm lovin it!
i'd really appreciate further and more in depth uploads to this topic (and all the others)!
keep up the great work!
greetings from switzerland :-)

[hammadshaikhha]

This is one of my favourite videos from the longer 30 minute videos. The problem is easy to understand, the data was well described, nice simple live coding. Great job, looking forward to more videos related to sports.

[mmcodesso]

Really liked you share the preprocessing process. Great :D

[waelhussein]

Love your videos! Topic, explanation, and production. Thank you very much!

[bilbo]

Great content as always. Thank you!! Would love too see more soccer ml takes.

[AmmarMalik]

This is one of the topics that always intrigued me. Please do make further videos related to Sports Analytics :) and thanks for this one by the way :)

[alisalehi]

Thanks for the great video.
Just one important point: you can not compare the algorithms by some default parameters and then pick the "best" one based on this naive experiment and then work on the winner's parameters to boost the results. In this video, you simply chose the XGBoost because, in the first experiment, its accuracy was higher than the others whereas the SVM is very sensitive to its parameters and one should spend a good amount of time on parameter selection for SVM before going through any conclusion.

[strengthtalks]

Very nice and well explained! Definately interested in ML for sports bets!

[FastestPodcastClips]

Where do i get the same dataset ? Because my dataset dont have ['HTGD', 'ATGD', 'HTP', 'ATP', 'DiffLP'] and i have a lot more features then 12

[larryguo]

Hi Siraj, great video, ...inspiring.!!

Just one question, ..after using Scatter_Matrix, we found out there are 'correlated features', ,,,shouldn't we eliminate those correlated ones ? ..

[jibbyjames]

This is a very interesting topic! I'd be interested in seeing a further sports analytics video, like you mentioned at around 14:25 mins

[harshkondkar]

Hey Siraj! Great Video Bro.
Guess I am a bit late to the party. Would love to see more videos on Football Analytics.
Keep Rocking!!

[ThePikmania]

I have had this idea in my mind for some days now and now you do a video about it. Spooky.

[quebono]

Im waiting so long for such a vidieo. Siraj please do more of them. Orange3 is a nice tool to quickly compare strategies. Could you also make a video about orange3. That would be nice. THX a lot ;)

[jonatanisse]

28:35 5, I´m not sure that's accurate Siraj. Anyhow, great video as always, keep up the good work champ!