Data Science

K-means clustering is a simple method for partitioning $n$ data points in $k$ groups, or clusters. Essentially, the process goes as follows: 1. Select $k$ centroids. These will be the center point for each segment. 2. Assign data points to nearest centroid. 3. Reassign

Term frequency-inverse document frequency (TF-IDF) vectorization is a mouthful to say, but it's also a simple and convenient way to characterize bodies of text. Due to its simplicity, this method scales better than some other topic modeling techniques (latent dirichlet allocation, probabilistic