Practicalmachinelearning: Machine Learning Resources for Graph Mining

Habedi's Practicalmachinelearning repository, updated in October 2024, provides a collection of Python-based machine learning resources including Jupyter and Zeppelin notebooks. It covers diverse domains such as anomaly detection, graph mining, and deep neural networks using libraries like scikit-learn and pandas.

Use Cases

• Detecting outliers using anomaly-detection labels in scikit-learn scripts
• Analyzing network structures using graph-mining algorithms and features
• Processing tabular data using pandas-based data-analysis workflows

Strengths

• Includes implementations for specialized domains like graph mining and anomaly detection
• Utilizes standard Python stack including scikit-learn, pandas, and Jupyter
• Licensed under BSD-3-Clause for open-source reuse

Limitations

• Lack of structured metadata regarding specific record counts or file sizes
• Primarily a resource collection rather than a curated, large-scale benchmark dataset

Provenance

Source

habedi (GitHub user)

Collection Method

Curated collection of scripts and notebooks

Freshness

Last updated October 2024.