Automatic normalisation of a data frame to third normal form, with
  the intention of easing the process of data cleaning. (Usage to design your
  actual database for you is not advised.)
  Originally inspired by the 'AutoNormalize' library for 'Python' by 'Alteryx'
  (<https://github.com/alteryx/autonormalize>), with various changes and
  improvements. Automatic discovery of functional or approximate dependencies,
  normalisation based on those, and plotting of the resulting "database" via
  'Graphviz', with options to exclude some attributes at discovery time, or
  remove discovered dependencies at normalisation time.

An interface to the 'AutoDesk' 'API' Platform including the Authentication 
    'API' for obtaining authentication to the 'AutoDesk' Forge Platform, Data Management 
    'API' for managing data across the platform's cloud services, Design Automation 'API'
    for performing automated tasks on design files in the cloud, Model
    Derivative 'API' for translating design files into different formats, sending
    them to the viewer app, and extracting design data, and Viewer for rendering 
    2D and 3D models.

A stacking solution for modeling imbalanced and severely skewed data. It automates the process of building homogeneous or heterogeneous stacked ensemble models by selecting "best" models according to different criteria. In doing so, it strategically searches for and selects diverse, high-performing base-learners to construct ensemble models optimized for skewed data. This package is particularly useful for addressing class imbalance in datasets, ensuring robust and effective model outcomes through advanced ensemble strategies which aim to stabilize the model, reduce its overfitting, and further improve its generalizability.

Automated data quality auditing using unsupervised machine learning.
    Provides AI-driven anomaly detection for data quality assessment, primarily
    designed for Electronic Health Records (EHR) data, with benchmarking
    capabilities for validation and publication. Methods based on: Liu et al.
    (2008) <doi:10.1109/ICDM.2008.17>, Breunig et al. (2000)
    <doi:10.1145/342009.335388>.