Principal Component Analysis

In this video, we will introduce principal component analysis or PCA for short - a tool to project multidimensional data to two dimentions. We will present our data on a scatterplot and visulize its structure.
This video is a part of Introduction to Data Science video series that dives into machine learning, visual analytics, and joys of interactive data analysis using Orange Data Mining software (orangedatamini...).
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The development of this video series was supported by grants from the Slovenian Research Agency (including P2-0209, V2-2274, and L2-3170), Slovenia Ministry of Digital Transformation, European Union (including xAIM and ARISA) and Google.org/Tides foundation.
#machinelearning #orange #visualanalytics #datamining
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Written by: Blaž Zupan (biolab.si/blaz)
Presented by: Noah Novšak
Production and edit: Lara Zupan
Intro/outro: Agnieszka Rovšnik
Music by: Damjan Jović - Dravlje Rec
Orange is developed by Biolab at University of Ljubljana (www.biolab.si)
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[@shipment7095]

Thanks!

[@kalicharanm1985]

I love the introductory music

[@mimirscomputationalwell]

How can one also plot the loadings?

[@zablociak]

PCA node - (Components->Data) ->Transpose node [select "From variable: components"] - (Data) -> Scatter Plot node or Data Table

[@tranngoc4350]

what happen if i use 3 PC? how can i explain it?

[@OrangeDataMining]

You can use the Components output and observe it in a Data Table. These will show the eigenvectors for each component.

[@staschernyshov6447]

They explained, how PCA helps visualize in 2 dimensions. And you are asking about PC3?

[@HariVl-pg7mp]

Really good❤ what software you are using?