Machine Learning
Recipe Text Clustering
An unsupervised text clustering project comparing bag-of-words and sentence embeddings for recipe-name clustering, using K-means, DBSCAN, and UMAP visualization.
Period
May 2026
Tools / Tech
PythonScikit-learnK-meansDBSCANSentenceTransformerUMAP
Why I built it
This project helped me test how different text representations change clustering behavior on short, noisy recipe names.
Links
What it includes
- Compares bag-of-words features with sentence embeddings.
- Runs K-means and DBSCAN clustering.
- Uses UMAP to visualize clusters and inspect whether groups are meaningful.
What I worked on
- Prepared text representations and clustering experiments.
- Compared algorithm behavior across sparse text features and dense embeddings.
- Completed the clustering notebook myself and reviewed outputs qualitatively instead of assuming labels were correct.
What I Learned
- Learned why short-text clustering is sensitive to representation choices.
- Practiced evaluating unsupervised learning without ground-truth labels.
- Built intuition for when visualization helps and when it can mislead.