Code Maktaba : Creating risk models for loan default prediction

Code Maktaba is a hands on training event series aimed at building and improving technical skills of Ai Kenya community members.

In our first event for 2019, we will be focusing on solving a loan default prediction challenge with the guidance of an experienced data scientist. The challenge was designed by Data Science Nigeria and is currently hosted on Zindi.

About the challenge
SuperLender is a local digital lending company, which prides itself in 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-science-nigeria-challenge-1-loan-default-prediction

Who is this training for? :
Aspiring and experienced data scientists.
Anyone with intermediate or expert level experience with Python and Jupyter Notebooks

Requirements? : (BYOD)Bring Your Own Device with Python and Jupyter Notebooks installed :)
Carry your ID/Passport for identification at the building.

Program
9.00am - 9.30am : Event Check-in
9.30am - 12 pm: Training led by Billy Odera, data scientist at Commercial Bank of Africa.
12pm - 12.30pm: Networking and close of event.

Event Filming notice: Please be advised that this event will be recorded for distribution use on our Youtube channel. Your entrance into the event will serve as your voluntary acceptance to appear in any footage exhibited throughout the world in perpetuity. Apologies for any inconvenience and thank you for your cooperation.