https://github.com/baschin1103/principal_component_analysis
In this repository you find a python program and the prints and 3D-visualization of it. After the KNN-Classification I wanted to know which variables have the most relevance for the results. One approach for this is the Principal-Component-Analysis (PCA). More details in the python program as comments.
https://github.com/baschin1103/principal_component_analysis
3d-printing labelencoder matplotlib numpy pandas principal-component-analysis sklearn-library sqlalchemy standardization variable-importance
Last synced: 12 months ago
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In this repository you find a python program and the prints and 3D-visualization of it. After the KNN-Classification I wanted to know which variables have the most relevance for the results. One approach for this is the Principal-Component-Analysis (PCA). More details in the python program as comments.
- Host: GitHub
- URL: https://github.com/baschin1103/principal_component_analysis
- Owner: Baschin1103
- Created: 2025-02-01T14:07:01.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-01T14:39:44.000Z (about 1 year ago)
- Last Synced: 2025-02-01T15:35:05.888Z (about 1 year ago)
- Topics: 3d-printing, labelencoder, matplotlib, numpy, pandas, principal-component-analysis, sklearn-library, sqlalchemy, standardization, variable-importance
- Language: Python
- Homepage:
- Size: 136 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
After the KNN-Classification I wanted to know which variables have the most relevance for the results. One approach for this is the Principal-Component-Analysis (PCA). It tries to create principal components (PC's) out of the variables so that less information gets lost. It is done here with the help of the sklearn-library.