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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development :: Build Tools"
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        "description": "# woeBinningPandas\nThis 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.\n\nThe 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.\n# Used programs versions\nSpyder (Python 3.7)\n\nPandas 0.23.4\n# Used Python libraries\nimport pandas as pd\n\nimport numpy as np\n\nimport math\n\nimport warnings\n\nimport copy\n# Using\n### Cloning a repository from GitHub\n#### Use Git CMD\n\n>cd YOUR LINK FOLDER\n\n>git clone https://github.com/V1ad98/woeBinningPandas.git\n\n#### File woeBinningPandas is ready to go!\n### Set your variable CSV file\n> yourvariable = pd.read_csv('Yourfile.csv')\n### Set the df variable and specify the column names from your CSV file, which you want to use.\n> df = yourvariable[['columnnames1', 'columnnames2','columnnames3']]\n### At THE END of the code in the function call woe_binning set the values of the arguments\n> binning = woe_binning(df, target_var, pred_var, min_perc_total, min_perc_class, stop_limit, abbrev_fact_levels, event_class)\n\n**df** - Name of data frame with input data.\n\n**target_var** - Name of dichotomous target variable in quotes. Only target variables with two distinct values (0 or 1).\n\n**pred_var** - Name of predictor variables to be binned in quotes. Values can be either numeric or factors.\n\n**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.\n\n**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. \nSetting 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.\n\n**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.\nstop_limit=0 will skip any WOE based merging.\nIncreasing the stop_limit will simplify the binning solution and may avoid overfitting. Accepted range: 0-0.5; default: 0.1.\n\n**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.\n\n**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).\nThis class will be represented by negative WOE values then.\n# Using with PIP package\n### Download PIP package woeBinningPandas\n> pip install woeBinningPandas\n### Add use package\n> import woeBinningPandas\n### Set variables and call a function\n> yourvariable = woeBinningPandas.pd.read_csv('Yourfile.csv')\n\n> df = yourvariable[['columnnames1', 'columnnames2','columnnames3']]\n### Pass your values to functions\n> binning = woeBinningPandas.woe_binning (df, target_var, pred_var, min_perc_total, min_perc_class, stop_limit, abbrev_fact_levels, event_class)\n# Examples\n> import woeBinningPandas\n\n> germancredit = woeBinningPandas.pd.read_csv('GermanCredit.csv')\n\n> df = germancredit[['credit_risk', 'amount','duration']]\n\n> binning = woeBinningPandas.woe_binning(df, 'credit_risk', 'duration', 0.05, 0, 0.1, 50, 'bad')\n\n\n",
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