User Clustering Model
This repository contains models and artifacts for a user clustering pipeline.
Models
- Preprocessor (OneHotEncoder + StandardScaler)
- UMAP reducer for dimensionality reduction
- KMeans clustering model with k=15
Metrics
- Best silhouette score on training: 0.4733
- Recommended silhouette score threshold for triggering auto retrain: 0.4
Files
preprocessor.joblib : preprocessing pipelineumap_reducer.joblib : UMAP reducerkmeans_model.joblib : KMeans modeltop_categories.json : top categories for cardinality limitingcluster_sizes.png : cluster distribution plotmetadata.json : metadata JSON with metrics and parameters
Usage
Load the models using joblib.load(), preprocess incoming data with the preprocessor, transform with UMAP, then predict clusters using KMeans.
Auto retrain can be triggered if silhouette score on new data falls below 0.4.
License
Specify your license here.
Generated and pushed by your clustering pipeline.
