https://github.com/anas436/vehicle-distinctive-clustering-using-hierarchical-clustering-with-python

https://github.com/anas436/vehicle-distinctive-clustering-using-hierarchical-clustering-with-python

agglomerative-clustering clustering distance-matrix jupyterlab matplotlib numpy pandas pylab python3 scikit-learn scipy sklearn

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/anas436/vehicle-distinctive-clustering-using-hierarchical-clustering-with-python
• Owner: Anas436
• Created: 2022-09-07T18:48:17.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-09-08T17:47:26.000Z (over 2 years ago)
• Last Synced: 2025-02-01T15:30:30.610Z (3 months ago)
• Topics: agglomerative-clustering, clustering, distance-matrix, jupyterlab, matplotlib, numpy, pandas, pylab, python3, scikit-learn, scipy, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 560 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Vehicle-Distinctive-Clustering-using-Hierarchical-Clustering-with-Python

## Objectives

After completing this lab you will be able to:

*   Use scikit-learn to do Hierarchical clustering

*   Create dendograms to visualize the clustering

Table of contents




    


        
Hierarchical Clustering - Agglomerative

            


                
Generating Random Data

                
Agglomerative Clustering

                
Dendrogram Associated for the Agglomerative Hierarchical Clustering

            
            

        
Clustering on the Vehicle Dataset

            


                
Data Cleaning

                
Clustering Using Scipy

                
Clustering using scikit-learn

            

    








Clustering on Vehicle dataset


Imagine that an automobile manufacturer has developed prototypes for a new vehicle. Before introducing the new model into its range, the manufacturer wants to determine which existing vehicles on the market are most like the prototypes--that is, how vehicles can be grouped, which group is the most similar with the model, and therefore which models they will be competing against.

Our objective here, is to use clustering methods, to find the most distinctive clusters of vehicles. It will summarize the existing vehicles and help manufacturers to make decision about the supply of new models.