Kiva.org is an online crowdfunding platform to extend financial services to poor and financially excluded people around the world. Kiva lenders have provided over $1 billion dollars in loans to over 2 million people. In order to set investment priorities, help inform lenders, and understand their target communities, knowing the level of poverty of each borrower is critical. However, this requires inference based on a limited set of information for each borrower.

In Kaggle Datasets' inaugural Data Science for Good challenge, Kiva is inviting the Kaggle community to help them build more localized models to estimate the poverty levels of residents in the regions where Kiva has active loans. Unlike traditional machine learning competitions with rigid evaluation criteria, participants will develop their own creative approaches to addressing the objective. Instead of making a prediction file as in a supervised machine learning problem, submissions in this challenge will take the form of Python and/or R data analyses using Kernels, Kaggle’s hosted Jupyter Notebooks-based workbench.

Kiva has provided a dataset of loans issued over the last two years, and participants are invited to use this data as well as source external public datasets to help Kiva build models for assessing borrower welfare levels. Participants will write kernels on this dataset to submit as solutions to this objective and five winners will be selected by Kiva judges at the close of the event. In addition, awards will be made to encourage public code and data sharing. With a stronger understanding of their borrowers and their poverty levels, Kiva will be able to better assess and maximize the impact of their work.

Honoured to receive the prize for the kernel Kiva Data Analysis w/ Naive Poverty Metric