Metadata-Version: 2.1
Name: mlpkg
Version: 0.3
Summary: ML package
Home-page: https://https://github.com/Sakshi-agarwal8/DeepLeaning
Author: Sakshi Agarwal
Author-email: sakshi.agg23@gmail.com
License: MIT
Description: # ML Package
        
        A Package through which multiple things can be done in a go like fitting a model of your own choice (regression/classification) and get final results in terms of model accuracy/ score/ mse/ mae/ confusion matrix and direct submission file which we need to upload in case of competitions.
        
        Two things can be achieved from this package:
        1. Results after fitting any Regression/classification algorithm of your own choice. Function to be used - **train_model**
        2. Final submission file to be submitted directly in competitions. Function to be used - **return_csv**
        
        ### Requirements:
        1. sklearn
        2. numpy
        3. pandas
        4. Pre-processed data to be passed - data already cleaned, splitted into train and test
        5. In **train_model** function, parameters to be passed:
           * x_train, y_train, x_test, y_test
           * model object
           * regression=True     *(if addressing regression problem)*
           * regression=False     *(if addressing classification problem (default is True))*
        6. In **return_csv** function, parameters to be passed:
           * x_train, y_train, x_test, y_test
           * model object
           * sample submission file   *(dataframe)*
           * target column name    *(string)*
           * file location name    *(filepath where to be saved)*
        
        ### Installation & Usage
        1. Make sure that your pip version is up-to-date: pip install --upgrade pip. Check version with pip -V. 
        2. Select the correct package:
           * There are two packages (two versions of the package )and you should SELECT ONLY ONE OF THEM which is the latest one.
           * Install using `pip install mlpkg` with latest version
        3. Import the package and use its functions: 
           `from ML_Utility import ML_package`
           
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Description-Content-Type: text/markdown
