AirQo Ugandan Air Quality Forecast Challenge
Helping Uganda
$5 000 USD
Completed (almost 6 years ago)
Forecast
902 joined
327 active
Start
Mar 14, 20
Close
May 31, 20
Reveal
May 31, 20
'%3e%3ccircle r='20' fill='%23008'/%3e%3ccircle r='17.5' fill='%23fff'/%3e%3ccircle r='3.5' fill='%23008'/%3e%3cg id='d'%3e%3cg id='c'%3e%3cg id='b'%3e%3cg id='a' fill='%23008'%3e%3ccircle r='.875' transform='rotate(7.5 -8.75 133.5)'/%3e%3cpath d='M0 17.5.6 7 0 2l-.6 5L0 17.5z'/%3e%3c/g%3e%3cuse xlink:href='%23a' transform='rotate(15)'/%3e%3c/g%3e%3cuse xlink:href='%23b' transform='rotate(30)'/%3e%3c/g%3e%3cuse xlink:href='%23c' transform='rotate(60)'/%3e%3c/g%3e%3cuse xlink:href='%23d' transform='rotate(120)'/%3e%3cuse xlink:href='%23d' transform='rotate(-120)'/%3e%3c/g%3e%3c/svg%3e){kind=small}
PCA
Data · 24 May 2020, 12:05 · 2
Since the number of features is large ( 121 x 6 + few extra features), I thought I could lower the dimension by using PCA. But I see that we can't perform PCA over data containing Nan values. Any suggestions on how I can go forward without having to impute data?
I think you cannot perform a PCA if you have nans, since it is a basically a matrix factorization. One way or another, you'll have to impute missing values with a certain strategy
Yeah, makes sense, thanks pednt!