The COVID-19 Dataset Clustering Analysis project explores a COVID-related dataset using principal component analysis (PCA) and clustering algorithms. The goal is to identify patterns and groupings in the data to gain insights into the spread and impact of COVID-19.
- Principal Component Analysis (PCA): Reduces the dimensionality of the dataset to help visualize and interpret complex data more easily.
- K-Means Clustering: Applies K-Means clustering to group data points into distinct clusters based on their features.
- Agglomerative Clustering: Uses hierarchical clustering to build a hierarchy of clusters and identify groupings within the data.