{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"Imports datasets from the UC Irvine Machine Learning Repository directly into Python scripts and notebooks, returning data as pandas dataframes along with structured metadata.","skillfed_tags":["dataset-loader","machine-learning","benchmark-data"],"use_cases":["Rapidly prototype machine learning models by fetching well-known benchmark datasets without manual download steps.","Build reproducible ML experiments that reference datasets by UCI ID, making scripts portable across environments.","Inspect dataset metadata (number of instances, feature types, missing values) before deciding which dataset to use for a task.","Automate data pipeline setup in notebooks where you need to load multiple UCI datasets and combine them.","Access dataset DOIs and citations programmatically to properly attribute data sources in research or reports."],"what_it_does":"ucimlrepo is a lightweight wrapper around the UC Irvine Machine Learning Repository that lets you programmatically fetch datasets into your Python environment. Instead of manually downloading CSV files from the web, you call fetch_ucirepo() with a dataset ID or name, and it returns a structured object containing the data as pandas dataframes (features, targets, IDs) plus rich metadata (dataset size, feature types, DOI, creators, abstract, and more). The package also provides list_available_datasets() to browse what's available.\n\nIt's designed for machine learning workflows where you want to quickly load benchmark datasets for experimentation or model development. The metadata access lets you inspect dataset properties programmatically before training, and the data is already split into features and targets, reducing boilerplate. Dependencies are minimal (pandas and certifi), and installation is straightforward.","worth_installing":"Yes, if you regularly work with UCI ML Repository datasets. The package eliminates manual download friction and provides programmatic metadata access. Maintenance is dormant (last release 815 days ago), so don't expect rapid bug fixes or new features, but the core functionality is stable and the low dependency count reduces risk. No known vulnerabilities. Install it for convenience in exploratory ML work; avoid it if you need active support or frequent dataset updates."},"id":"ucimlrepo","links":{"html":"https://skillfed.io/packages/ucimlrepo","md":"https://skillfed.io/packages/ucimlrepo.md","pypi":"https://pypi.org/project/ucimlrepo/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-05-21","license_spdx":null,"license_treatment":"permissive","name":"ucimlrepo","python_support":"supports_current","summary":"Package to easily import datasets from the UC Irvine Machine Learning Repository into scripts and notebooks."},"popularity":{"monthly_downloads":320829,"position":7631,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.0.7"}