Machine Learning Helper

This package usage multiple algorithms and parameters to accomodate different set of use cases to help in creating multiple machine learning algorithms.

1.0 woe (Weight of Evidence):

This function will help to calculate Weight of Evidence and Information Value, the charts can be displayed and coarse classing can also be done.

1.1 Parameters:

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• max_bin: int Maximum number of bins for numeric variables. The default is 10
• iv_threshold: float Threshold value for Information Value. Variables with higher than threshold will be considered for transformation
• ignore_threshold: Boolean This parameter controls whether the defined threshold should be considered or ignored. The default is 'True'

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1.2 Returns:

DataFrame having weight of evidence of each column along with the target variable

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1.3 Approach:

1. Create an instance of woe my_woe = woe()

2. Call fit method on the defined object by passing on dataframe and the target variable name my_woe.fit(df,target)

3. Call the transform method transformed_df = my_woe.transform()

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Example

Create Sample DataFrame

from mlh import woe
import pandas as pd
import numpy as np
import random

seed=1456
np.random.seed(seed)
random.seed(seed)

rows = 1000

y = random.choices([0,1],k=rows,weights=[.7,.3])

x1 = random.choices(np.arange(20,40),k=rows)
x2 = np.random.randint(1000,2000,size=rows)
x3 = random.choices(np.arange(1,100),k=rows)
x4 = random.choices(['m','f','u'],k=rows)
x5 = random.choices(['a','b','c','d','e','f','g','h'],k=rows)

df = pd.DataFrame({'y':y,'x1':x1,'x2':x2,'x3':x3,'x4':x4,'x5':x5})

df.head()

Fitting and prediction

Create Instance of Weight of Evidence Package

my_woe = woe()

Fit the data with created instance

my_woe.fit(df,'y')

Display the relevant charts

my_woe.getWoeCharts()

Merge values of X3 Variable at 1 and 2 indices using the Weight of Evidence chart from the first Iteration

my_woe.reset_woe(2,(1,2),1)

Get latest Iteration Information Value

my_woe.get_IV()

Replace the original values in the Dataframe with Weight of Evidence

transformed_df = my_woe.transform()