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    "info": {
        "author": "Devendra Kumar Sahu",
        "author_email": "devsahu99@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
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        "description": "# Machine Learning Helper\n\nThis package usage multiple algorithms and parameters to accomodate different set of use cases to help in creating multiple machine learning algorithms.\n\n## 1.0 woe (Weight of Evidence):\nThis function will help to calculate Weight of Evidence and Information Value, the charts can be displayed and coarse classing can also be done.\n\n### 1.1 Parameters:\n------------------------------------------------------------\n* **max_bin**: int\n    Maximum number of bins for numeric variables. The default is 10\n* **iv_threshold**: float\n    Threshold value for Information Value. Variables with higher than threshold will be considered for transformation\n* **ignore_threshold**: Boolean\n    This parameter controls whether the defined threshold should be considered or ignored. The default is 'True'\n\n------------------------------------------------------------\n### 1.2 Returns:\nDataFrame having weight of evidence of each column along with the target variable\n\n\n------------------------------------------------------------\n### 1.3 Approach:\n\n1. Create an instance of woe\n     my_woe = woe()\n\n2. Call fit method on the defined object by passing on dataframe and the target variable name\n     my_woe.fit(df,target)\n\n3. Call the transform method\n    transformed_df = my_woe.transform()\n\n------------------------------------------------------------\n## Example\n\n### Create Sample DataFrame\n```python\nfrom mlh import woe\nimport pandas as pd\nimport numpy as np\nimport random\n\nseed=1456\nnp.random.seed(seed)\nrandom.seed(seed)\n```\n```python\nrows = 1000\n```\n```python\ny = random.choices([0,1],k=rows,weights=[.7,.3])\n```\n```python\nx1 = random.choices(np.arange(20,40),k=rows)\nx2 = np.random.randint(1000,2000,size=rows)\nx3 = random.choices(np.arange(1,100),k=rows)\nx4 = random.choices(['m','f','u'],k=rows)\nx5 = random.choices(['a','b','c','d','e','f','g','h'],k=rows)\n```\n```python\ndf = pd.DataFrame({'y':y,'x1':x1,'x2':x2,'x3':x3,'x4':x4,'x5':x5})\n```\n```python\ndf.head()\n```\n### Fitting and prediction\n\n**Create Instance of Weight of Evidence Package**\n```python\nmy_woe = woe()\n```\n**Fit the data with created instance**\n```python\nmy_woe.fit(df,'y')\n```\n**Display the relevant charts**\n```python\nmy_woe.getWoeCharts()\n```\n**Merge values of X3 Variable at 1 and 2 indices using the Weight of Evidence chart from the first Iteration**\n```python\nmy_woe.reset_woe(2,(1,2),1)\n```\n**Get latest Iteration Information Value**\n```python\nmy_woe.get_IV()\n```\n**Replace the original values in the Dataframe with Weight of Evidence**\n```python\ntransformed_df = my_woe.transform()\n```\n\n",
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        "name": "mlh",
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        "summary": "This package provides helper utilities for machine learning tasks. One major utility is calculation of weight of evidence",
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