AutoPrep

Enhancements

This release introduces a new feature to detect anomalies in your data and includes a critical bug fix that enhances the robustness of the pipeline creation process.

• Anomaly Detection Feature:
  We've added a new method to the AutoPrep class that allows you to easily find anomalies in your dataset. This feature helps you identify multivariate outliers and unusual data points that might require special attention or further investigation.

• Pipeline Initialization Bug:
  Fixed a bug where the pipeline construction would fail if one of the pipelines was None. The system now gracefully handles None pipelines, ensuring that the rest of your pipeline continues to function without errors.

Enhancements

• Cleaner Code:
  The codebase has been refactored for improved readability and maintainability. This update includes more efficient code organization, removal of redundant code, and optimization of existing functions to enhance overall performance.

• OOP Fixes:
  Several improvements have been made to the Object-Oriented Programming (OOP) structure. These fixes address class hierarchy issues, improve inheritance logic, and ensure better encapsulation and modularity within the codebase.

Enhancements:

• Bug Fix: Resolved an index-related issue affecting pattern recognition functionality.
• Code Improvements: Refactored the codebase for better performance, readability, and maintainability.
• Documentation: Added comprehensive Pydoc documentation to enhance code clarity and facilitate easier understanding for developers.

Upcoming Features:

Unsupervised Model Support: Planning to introduce an option for unsupervised pattern recognition in categorical data in future releases.

• Refactored code + minor fixes