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

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

Last synced: 3 months 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 (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-12-15T11:38:28.000Z (over 1 year ago)
• Last Synced: 2025-02-09T09:43:56.441Z (over 1 year ago)
• Topics: anova, dbscan, kmeans, machine-learning, matplotlib, numpy, pandas, pca, scikit-learn, seaborn, statistics
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/datasets/rohan0301/unsupervised-learning-on-country-data
• Size: 4.57 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.