HOME LAB

PART 2 : DA

 Data Analytics- Part 2

Disclaimer:

  • This document contains unedited notes and has not been formally proofread.
  • The information provided in this document is intended to provide a basic understanding of certain technologies.
  • Please exercise caution when visiting or downloading from websites mentioned in this document and verify the safety of the website and software.
  • Some websites and software may be flagged as malware by antivirus programs.
  • The document is not intended to be a comprehensive guide and should not be relied upon as the sole source of information.
  • The document is not a substitute for professional advice or expert analysis and should not be used as such.
  • The document does not constitute an endorsement or recommendation of any particular technology, product, or service.
  • The reader assumes all responsibility for their use of the information contained in this document and any consequences that may arise.
  • The author disclaim any liability for any damages or losses that may result from the use of this document or the information contained therein.
  • The author and publisher reserve the right to update or change the information contained in this document at any time without prior notice.

 

******************************************************************************

 

49. Unstructured Algorithm


Basics to see here on how to – find underlying factors or reduce the Dimensions using below techniques
·      Factor Analysis – FA
·      Recommendation Engine
·      Principal component Analysis - PCA
·      Singular Value decomposition – SVD
·      Eigenvalue decomposition – EVD
·      Clustering Methods
§  K – Means clustering
§  Hierarchy clustering
§  DB Scan
§  OPTICS

50. Factor Analysis – FA


·      FA is about to Pull out or explain hidden factors or underlying factors in their relationship of variables
·      The information received from these hidden factors can be used to reduce the number of set of variables
·      These number of factors are determined using Scree-plot.
·      There are a number of rotations: -
§  Varimax
§  Quartimax
·      FA looks for the correlation values
·      Factors that have similar overloading can be grouped into cluster

 51. Principal Component Analysis










Comments

Popular Posts

Chennai :MTC complaint cell Customer Care No.:+91-9445030516 /Toll Free : 18005991500

Marriage Registration Online steps [Tamil Nadu]

HOME LAB