Code Maktaba: Approaches to a credit scoring loan default challenge featuring Billy Odera

Описание: Billy Odera, a data scientist specialised in credit scoring systems provides insights, approaches and tips on solving the loan default challenge by Data science Nigeria that is hosted on Zindi.

About the challenge
SuperLender is a local digital lending company, which prides itself on its effective use of credit risk models to deliver profitable and high-impact loan alternative. Its assessment approach is based on two main risk drivers of loan default prediction:. 1) willingness to pay and 2) ability to pay. Since not all customers pay back, the company invests in experienced data scientists to build robust models to effectively predict the odds of repayment.

These two fundamental drivers need to be determined at the point of each application to allow the credit grantor to make a calculated decision based on repayment odds, which in turn determines if an applicant should get a loan, and if so - what the size, price and tenure of the offer will be.

Read more about the challenge on http://zindi.africa/competitions/data...

The Notebook being used by the speaker can be obtained on https://github.com/billy-odera/DNS-Ch...

Presentation slides from this training can be obtained on http://bit.ly/31TjjjG

Below are links to highlighted resources from the training :

ELI5 on https://pypi.org/project/eli5/

Loan Default Challenge and link to datasets : http://zindi.africa/competitions/data...

Jupyter Lab : https://jupyterlab.readthedocs.io/en/...

Working with Jupyter Notebooks on Visual studio : https://code.visualstudio.com/docs/py...

Anaconda distribution : https://www.anaconda.com/distribution/

GGPLot documentation : https://www.rdocumentation.org/packag...

Merge, join and concatenate : https://pandas.pydata.org/pandas-docs...

Weight of evidence and information: https://www.listendata.com/2015/03/we...

Yellowbrick: https://www.scikit-yb.org/en/latest/

Catboost: https://tech.yandex.com/catboost/

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