skorecard is a scikit-learn compatible python package that helps streamline the development of credit risk acceptance models (scorecards).

Scorecards are ‘traditional’ models used by banks in the credit decision process. Internally, scorecards are Logistic Regression models that make use of features that are binned into different groups. The process of binning is usually done manually by experts, and skorecard provides tools to makes this process easier. skorecard is built on top of scikit-learn as well as other excellent open source projects like optbinning, dash and plotly.

👉 Read the blogpost introducing skorecard

• Automate bucketing of features inside scikit-learn pipelines.
• Dash webapp to help manually tweak bucketing of features with business knowledge
• Extension to sklearn.linear_model.LogisticRegression that is also able to report p-values
• Plots and reports to speed up analysis and writing technical documentation.

skorecard offers a range of bucketers:

import pandas as pd
from skorecard.bucketers import EqualWidthBucketer

df = pd.DataFrame({'column' : range(100)})

ewb = EqualWidthBucketer(n_bins=5)
ewb.fit_transform(df)

ewb.bucket_table('column')
#>    bucket                       label  Count  Count (%)
#> 0      -1                     Missing    0.0        0.0
#> 1       0                (-inf, 19.8]   20.0       20.0
#> 2       1                (19.8, 39.6]   20.0       20.0
#> 3       2  (39.6, 59.400000000000006]   20.0       20.0
#> 4       3  (59.400000000000006, 79.2]   20.0       20.0
#> 5       4                 (79.2, inf]   20.0       20.0

That also support a dash app to explore and update bucket boundaries:

ewb.fit_interactive(df)
#> Dash app running on http://127.0.0.1:8050/

pip3 install skorecard

See ing-bank.github.io/skorecard/.