https://github.com/gregoritsch3/ml_eda_clustering_aidassessment

An EDA and Machine Learning Clustering exercise on the Country Aid Assessment dataset demonstrating the use of PCA, KMeans and DBSCAN clustering, Elbow Methods, etc. The clustering algorithm successfully demarcates countries that are in most dire need of aid based on their GDPP and Child Mortality rate.
https://github.com/gregoritsch3/ml_eda_clustering_aidassessment

dbscan kmeans machine-learning matplotlib numpy pandas pca scikit-learn seaborn

Last synced: 5 days ago
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An EDA and Machine Learning Clustering exercise on the Country Aid Assessment dataset demonstrating the use of PCA, KMeans and DBSCAN clustering, Elbow Methods, etc. The clustering algorithm successfully demarcates countries that are in most dire need of aid based on their GDPP and Child Mortality rate.

• Host: GitHub
• URL: https://github.com/gregoritsch3/ml_eda_clustering_aidassessment
• Owner: Gregoritsch3
• Created: 2024-12-12T13:32:39.000Z (10 days ago)
• Default Branch: main
• Last Pushed: 2024-12-15T11:38:28.000Z (7 days ago)
• Last Synced: 2024-12-15T12:27:09.119Z (7 days ago)
• Topics: dbscan, kmeans, machine-learning, matplotlib, numpy, pandas, pca, scikit-learn, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 2.92 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# ML_EDA_Clustering_AidAssessment

An EDA and Machine Learning Clustering exercise on the Country Aid dataset demonstrating the use of PCA, KMeans and DBSCAN clustering, Elbow Methods, etc. The clustering algorithm successfully demarcates countries that are in most dire need of aid based on their GDPP and Child Mortality rate.