Search Results for "python feature selection"
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Showing 2 open source projects for "python feature selection"
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GiveLife365: Powering Nonprofits with Smart CRM.Managing constituents - donors, volunteers, events, and cases shouldn’t slow down your mission. GiveLife365 is a cloud-based CRM built for nonprofits, helping you streamline operations, boost engagement, and measure real impact—all in one place.
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Use the Filerev app to organize Google Drive and reduce storage costs.Use the Filerev app to organize Google Drive and reduce storage costs. The Duplicate File Finder quickly shows all duplicates in Google Drive without downloading all of your files. The Storage Analyzer lets you browse your folders by size in Google Drive. You can also view your Google Drive files in different categories such as: hidden / orphaned files, large files, empty files, empty folders, large folders, old files, temporary files, files by extension. Every category includes powerful filters and the ability to bulk delete your files in Google Drive. Plus, there are charts and graphs to help you quickly see how your storage space is being used and the number of files and specific types of files or folders that are consuming the most space. You can get started for free to see what is consuming your Google Drive storage space and quickly clean up the clutter.
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Zylthra
Zylthra: A PyQt6 app to generate synthetic datasets with DataLLM.
Welcome to Zylthra, a powerful Python-based desktop application built with PyQt6, designed to generate synthetic datasets using the DataLLM API from data.mostly.ai. This tool allows users to create custom datasets by defining columns, configuring generation parameters, and saving setups for reuse, all within a sleek, dark-themed interface. -
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Twinify
Privacy-preserving generation of a synthetic twin to a data set
twinify is a software package for the privacy-preserving generation of a synthetic twin to a given sensitive tabular data set. On a high level, twinify follows the differentially private data-sharing process introduced by Jälkö et al.. Depending on the nature of your data, twinify implements either the NAPSU-MQ approach described by Räisä et al. or finds an approximate parameter posterior for any probabilistic model you formulated using differentially private variational inference (DPVI)....
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