Principal Component Analysis in Python – and its Most Common Applications!

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Principal Component Analysis is a widely used data analysis technique that can identify patterns in large datasets. It has been applied to fields as diverse as astronomy, psychology and even marketing!

PCA in Python: Explained

PCA is a statistical technique that can reduce the dimensionality of data sets by transforming them into new sets of orthogonal (uncorrelated) variables called principal components or eigenvectors. PCA is a data analysis technique that reduces the dimensionality of data to reveal patterns. It's an essential method in many fields, including machine learning, bioinformatics and statistical computing.

When to use PCA?

  • Whenever you need to ensure that variables in data are independent of each other.
  • When you need to reduce the number of variables in a data set with different variables in it.
  • When you need to interpret variable & data selection out of it.

Some Common Applications of Principal Component Analysis (PCA)

Principal Component Analysis performs well in identifying various influencing factors affecting results in particular areas. It can correlate factors associated with a candidate who might be winning/losing. In the election commission, the PCA technique is also used in many applications, different industries, & multiple fields. Some are discussed below:

  • Image compression: PCA can be employed in image compression and can resize the image as per the requirements while determining different patterns.
  • Customer profiling: Principal Component Analysis helps in Customer profiling based on demographics & their intellect in the purchase.
  • Research: PCA is a widely known technique widely used by researchers in different fields, especially food science.
  • Banking: It can also be used in banking for activities like filing applicants' names for loans, credit cards, etc.
  • Maintaining Customer Perception towards brands.
  • Finance: PCA is used diversely in the field of Finance to analyze stocks quantitatively, forecast portfolio returns, and interest rate implantation.
  • Medical and Healthcare: PCA is also used in the Healthcare sector and related areas like patient insurance data. There are multiple sources of data with a vast number of variables correlated to each other. Probable resources are hospitals, pharmacies, etc.

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