https://github.com/bhattbhavesh91/pca-from-scratch-iris-dataset

Implementing PCA from Scratch for iris dataset
https://github.com/bhattbhavesh91/pca-from-scratch-iris-dataset

iris-dataset pca principal-component-analysis

Last synced: 4 months ago
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Implementing PCA from Scratch for iris dataset

• Host: GitHub
• URL: https://github.com/bhattbhavesh91/pca-from-scratch-iris-dataset
• Owner: bhattbhavesh91
• License: gpl-3.0
• Created: 2017-08-25T11:22:53.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2019-10-14T05:14:41.000Z (about 6 years ago)
• Last Synced: 2025-03-29T06:11:21.640Z (7 months ago)
• Topics: iris-dataset, pca, principal-component-analysis
• Language: Jupyter Notebook
• Size: 375 KB
• Stars: 25
• Watchers: 2
• Forks: 14
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
## PCA on IRIS Dataset

The main purposes of a principal component analysis are the analysis of data to identify patterns and finding patterns to reduce the dimensions of the dataset with minimal loss of information.

Here, our desired outcome of the principal component analysis is to project a feature space (our dataset consisting of n-dimensional samples) onto a smaller subspace that represents our data "well". A possible application would be a pattern classification task, where we want to reduce the computational costs and the error of parameter estimation by reducing the number of dimensions of our feature space by extracting a subspace that describes our data "best".

# Principal Component Analysis (PCA) from Scratch in Python

## To view the video

* [Click here](https://youtu.be/uFbDWu0tDrE)

* Click on the image below

[![Principal Component Analysis (PCA) from Scratch in Python](http://img.youtube.com/vi/uFbDWu0tDrE/0.jpg)](http://www.youtube.com/watch?v=uFbDWu0tDrE)

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