ed-creator/costa-rican-household-poverty-prediction

Costa Rican Household Poverty Prediction

A Kaggle Competition

Description

The Inter-American Development Bank is asking the Kaggle community for help with income qualification for some of the world's poorest families. Are you up for the challenge?

Here's the backstory: Many social programs have a hard time making sure the right people are given enough aid. It’s especially tricky when a program focuses on the poorest segment of the population. The world’s poorest typically can’t provide the necessary income and expense records to prove that they qualify.

In Latin America, one popular method uses an algorithm to verify income qualification. It’s called the Proxy Means Test (or PMT). With PMT, agencies use a model that considers a family’s observable household attributes like the material of their walls and ceiling, or the assets found in the home to classify them and predict their level of need.

While this is an improvement, accuracy remains a problem as the region’s population grows and poverty declines.

To improve on PMT, the IDB (the largest source of development financing for Latin America and the Caribbean) has turned to the Kaggle community. They believe that new methods beyond traditional econometrics, based on a dataset of Costa Rican household characteristics, might help improve PMT’s performance.

Beyond Costa Rica, many countries face this same problem of inaccurately assessing social need. If Kagglers can generate an improvement, the new algorithm could be implemented in other countries around the world.

This is a Kernels-Only Competition, so you must submit your code through Kernels, rather than uploading .csv predictions. You can create private Kernels and even share/edit your work with teammates by adding them as collaborators.

Evaluation

F1 Score will be used to evaluate the results