Metadata-Version: 2.1
Name: multilabeler
Version: 0.4
Summary: Prediction of two dependent labels
Home-page: https://github.com/owos/Multilabeler
Author: Abraham Owodunni
License: UNKNOWN
Description: 
        # Project Title
        
        This is a package that takes in X and y as training data and predicts two outputs as y1 and y2 for any given test data.
        This package removes the limitation of just have one target feature in machine learning.
        
        
        ## Contributing
        
        Contributions are always welcome!
        
        The package needs to be extend to predict non-categorical variables.
        
        Please adhere to this project's `code of conduct`.
        
          
        ## Installation 
        
        To install this package run
        
        ```bash
          pip install multilabeler
        ```
        
          
        ## Feedback
        
        If you have any feedback, please reach out to me at owodunniabraham@gmail.com
          
        ## License
        
        [MIT](https://choosealicense.com/licenses/mit/)
        
          
        ## Usage/Examples
        
        [Notebook](https://github.com/owos/Multilabel/blob/main/use_case_of_mutlilabel.ipynb)
        ```bash
          from multilabeler import BilableClassifier
          model = BilableClassifier(xgboost())
        ```
          
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
