[GitHub release](https://github.com/MitchMedeiros/MLCompare/tag/v1.2.0)
Pipelines
- Created a `data_pipeline` function for performing only data retrieval and processing
- Expanded the generated model performance metrics and added a required argument to `full_pipeline` for specifying whether the pipeline is being used for regression or classification tasks
DatasetProcessor
- Refactored the class to store the train-test split data for easier processing
- Added a `handle_nan` method which can drop, forward-fill, and backward-fill missing values
- Added label encoding ordinal encoding, and target encoding methods
- Added several scaling and transformation methods from sklearn: StandardScaler, MinMaxScaler, MaxAbsScaler, RobustScaler, PowerTransformer, QuantileTransformer, and Normalize
Documentation
- Created a new homepage
- Updated the layout of the API Reference page
- Added content to the Release Notes page
- Improved various docstrings
- Made multiple updates to the README including adding a "Planned Additions" section
Other
- Added a `ResultsWriter` class, responsible for directory and file naming and creation throughout pipelines
- Implemented directory and file name incrementing to prevent overwrites
- Changed the default directory name to use the current timestamp to ensure uniqueness
- Improved how saving model results is handled
- Removed the `DataProcessor` class in favor of pipelines
- Migrated several high-level functions being used within pipelines to a new module: `processing.py`
- Improved unit test coverage