Metadata-Version: 1.1
Name: woeBinningPandas
Version: 1.9
Summary: My package from github repo.
Home-page: https://github.com/V1ad98/woeBinningPandas.git
Author: V1ad98
Author-email: 47775603+V1ad98@users.noreply.github.com
License: MIT
Description: # woeBinningPandas
        This code generates a supervised fine and coarse classing of numeric variables and factors with respect to a dichotomous target variable. Its parameters provide flexibility in finding a binning that fits specific data characteristics and practical needs.
        
        The basis of this code was taken woeBinning code (https://github.com/cran/woeBinning/blob/master/R/woe.binning.R) and changed from R to Python.
        # Used programs versions
        Spyder (Python 3.7)
        
        Pandas 0.23.4
        # Used Python libraries
        import pandas as pd
        
        import numpy as np
        
        import math
        
        import warnings
        
        import copy
        # Using
        ### Cloning a repository from GitHub
        #### Use Git CMD
        
        >cd YOUR LINK FOLDER
        
        >git clone https://github.com/V1ad98/woeBinningPandas.git
        
        #### File woeBinningPandas is ready to go!
        ### Set your variable CSV file
        > yourvariable = pd.read_csv('Yourfile.csv')
        ### Set the df variable and specify the column names from your CSV file, which you want to use.
        > df = yourvariable[['columnnames1', 'columnnames2','columnnames3']]
        ### At THE END of the code in the function call woe_binning set the values of the arguments
        > binning = woe_binning(df, target_var, pred_var, min_perc_total, min_perc_class, stop_limit, abbrev_fact_levels, event_class)
        
        **df** - Name of data frame with input data.
        
        **target_var** - Name of dichotomous target variable in quotes. Only target variables with two distinct values (0 or 1).
        
        **pred_var** - Name of predictor variables to be binned in quotes. Values can be either numeric or factors.
        
        **min_perc_total** - For numeric variables this parameter defines the number of initial classes before any merging is applied. WOE starts. Increasing the min_perc_total} parameter will avoid sparse bins. Accepted range: 0.0001-0.2; default: 0.05.
        
        **min_perc_ class** - If a column percentage of one of the target classes within a bin is below this limit (e.g. below 0.01=1\%) then the respective bin will be joined with others. In case of numeric variables adjacent predictor classes are merged. 
        Setting min_perc_class > 0 may provide more reliable WOE values. Accepted range: 0-0.2; default: 0, i.e. no merging with respect to sparse target classes is applied.
        
        **stop_limit** - Stops WOE based merging of the predictor's classes/levels in case the resulting information value (IV) decreases more than (e.g. 0.05 = 5%) compared to the preceding binning step.
        stop_limit=0 will skip any WOE based merging.
        Increasing the stop_limit will simplify the binning solution and may avoid overfitting. Accepted range: 0-0.5; default: 0.1.
        
        **abbrev_fact_levels** - Abbreviates the names of new (merged) factor levels via the base abbreviate function in case the specified number of characters is exceeded.
        
        **event_class** - Optional parameter for specifying the class of the target event. This class typically indicates a negative event like a loan default or a disease. Use characters in quotes (e.g. bad).
        This class will be represented by negative WOE values then.
        # Using with PIP package
        ### Download PIP package woeBinningPandas
        > pip install woeBinningPandas
        ### Add use package
        > import woeBinningPandas
        ### Set variables and call a function
        > yourvariable = woeBinningPandas.pd.read_csv('Yourfile.csv')
        
        > df = yourvariable[['columnnames1', 'columnnames2','columnnames3']]
        ### Pass your values to functions
        > binning = woeBinningPandas.woe_binning (df, target_var, pred_var, min_perc_total, min_perc_class, stop_limit, abbrev_fact_levels, event_class)
        # Examples
        > import woeBinningPandas
        
        > germancredit = woeBinningPandas.pd.read_csv('GermanCredit.csv')
        
        > df = germancredit[['credit_risk', 'amount','duration']]
        
        > binning = woeBinningPandas.woe_binning(df, 'credit_risk', 'duration', 0.05, 0, 0.1, 50, 'bad')
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
