Comparative clustering and visualization of socioeconomic and health indicators: A case of 47 counties in Kenya.

Overview:

This project implements data analysis and visualization techniques to explore various indicators related to a specific domain. It includes functionality for loading, preprocessing, and analyzing the data, as well as clustering and visualizing the results.

Features:

Data loading using a custom DataLoader class. Data preprocessing and analysis, including correlation calculation, PCA application, and dendrogram generation. K-means clustering of the data and visualization of the clustering results. Creation of interactive maps with clustered data points using Folium. Data manipulation for visualization purposes, including generating demo data and creating heatmap visualizations. Triangulation for heatmap visualization using Matplotlib.

Dependencies:

Python 3.x NumPy Matplotlib Selenium Pandas Folium Scikit-learn