https://github.com/tanyakuznetsova/multidimensional-scaling-of-european_cities
This project explores the spatial relationships between twenty European cities using classical manual Multidimensional Scaling (MDS), MDS from scikit-learn, and compares the results with Principal Component Analysis (PCA).
https://github.com/tanyakuznetsova/multidimensional-scaling-of-european_cities
classical-ml-algorithms dimensionality-reduction dimensionality-reduction-technique mds multidimensional-scaling pca principal-component-analysis unsupervised-learning unsupervised-machine-learning
Last synced: 8 months ago
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This project explores the spatial relationships between twenty European cities using classical manual Multidimensional Scaling (MDS), MDS from scikit-learn, and compares the results with Principal Component Analysis (PCA).
- Host: GitHub
- URL: https://github.com/tanyakuznetsova/multidimensional-scaling-of-european_cities
- Owner: tanyakuznetsova
- Created: 2024-01-08T14:10:13.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-08T14:29:04.000Z (almost 2 years ago)
- Last Synced: 2025-01-18T08:36:24.150Z (10 months ago)
- Topics: classical-ml-algorithms, dimensionality-reduction, dimensionality-reduction-technique, mds, multidimensional-scaling, pca, principal-component-analysis, unsupervised-learning, unsupervised-machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 272 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Multidimensional-Scaling-of-European_Cities
This project explores the spatial relationships between twenty European cities using classical manual Multidimensional Scaling (MDS), MDS from scikit-learn, and compares the results with Principal Component Analysis. It contains the visualizations and analysis results.
## Contents
- **Multidimensional scaling of European Cities.ipynb:** The Jupyter notebook containing the project.
- **README.md:** Brief overview of the project.
## Execution Environment
The project is developed and executed in a Google Colab environment.
## Execution Flow
1. Classical manual MDS: Implementing Multidimensional Scaling manually to explore spatial relationships.
2. MDS from scikit-learn: Utilizing the MDS implementation from scikit-learn library for comparison.
3. PCA Comparison: Applying Principal Component Analysis to compare results with MDS.
## Dependencies
- Python
- NumPy
- scikit-learn
- Matplotlib
- PCA
## Note
- The project discusses the spatial relationships between cities using dimensionality reduction techniques.
- MDS may involve manual rotation or mirroring to achieve desired visualizations.
- PCA might also result in rotated or mirrored visualizations depending on the orientation of principal components.
- ## Acknowledgements
- The distances between cities were obtained from https://www.distancecalculator.net/