Clustering-

in a dataset containing heterogeneous data—comprising different types such as numerical, categorical, and textual elements—introduces additional challenges to the clustering process. To effectively identify significant groupings within such a varied dataset, specialized approaches and algorithms are necessary.

Therefore, we might transform all data into a uniform type for analysis or focus on subsets of the data that are more directly comparable. In this instance, I’ve chosen to work exclusively with the latitude and longitude information to apply the DBSCAN algorithm. DBSCAN, which stands for Density-Based Spatial Clustering of Applications with Noise, excels at identifying clusters with irregular shapes in data of uneven densities.

This algorithm differentiates clusters based on areas of higher density, separated by regions of lower density, and does not require pre-defining the number of clusters, which is advantageous for datasets with an unknown number of clusters.

The two principal parameters of DBSCAN are ‘eps’, which is the maximum distance between two points for them to be considered neighbors, and ‘min samples’, which is the minimum number of points required for a dense region to be recognized as a cluster.