Can I use this skill with raw CSV data?

Yes, the skill can load data from CSV, TXT, and other common formats, and it automatically handles preprocessing steps like scaling to ensure optimal results.

Does it provide visual output for the clusters?

Yes, it generates visual results such as scatter plots with cluster labels to help you visually interpret the data structure and groupings.

What clustering algorithms are supported by this skill?

The skill supports a variety of popular algorithms including K-means, DBSCAN, and hierarchical clustering, typically implemented using the scikit-learn library.

How does it help with model performance evaluation?

It automatically calculates key performance metrics, such as the silhouette score and Davies-Bouldin index, to help you determine the quality of the clusters.

Is this skill useful for finding outliers?

Absolutely. By utilizing density-based algorithms like DBSCAN, the skill is highly effective at identifying anomalies and noise within your dataset.

Clustering Algorithm Runner

Name: Clustering Algorithm Runner
Author: intent-solutions-io

byintent-solutions-io

Data Science & ML

Executes advanced machine learning clustering algorithms to identify patterns, group data points, and perform unsupervised learning analysis.

About

This skill empowers Claude to perform sophisticated unsupervised machine learning tasks by executing clustering algorithms like K-means, DBSCAN, and hierarchical clustering. It streamlines the data science workflow by automatically handling data preprocessing, model selection, parameter tuning, and result visualization. Whether you are performing customer segmentation or anomaly detection, this skill provides the necessary Python infrastructure and statistical metrics to derive meaningful insights from raw datasets directly within your development environment.

Key Features

Automated visualization generation including cluster scatter plots
Intelligent outlier and anomaly detection capabilities
Automated K-means, DBSCAN, and hierarchical clustering execution
Integrated data preprocessing including scaling and normalization
0 GitHub stars
Comprehensive performance metrics like Silhouette and Davies-Bouldin indices

Use Cases

Discovering natural groupings and structures in large, unlabelled datasets
Customer segmentation for targeted marketing and behavioral analysis
Anomaly detection in network traffic logs or financial transactions

About

Key Features

Automated visualization generation including cluster scatter plots
Intelligent outlier and anomaly detection capabilities
Automated K-means, DBSCAN, and hierarchical clustering execution
Integrated data preprocessing including scaling and normalization
0 GitHub stars
Comprehensive performance metrics like Silhouette and Davies-Bouldin indices

Use Cases

Discovering natural groupings and structures in large, unlabelled datasets
Customer segmentation for targeted marketing and behavioral analysis
Anomaly detection in network traffic logs or financial transactions