DESCRIPTION Identify the level of income qualification needed for the families in Latin America

Problem Statement Scenario: Many social programs have a hard time making sure the right people are given enough aid. It’s tricky when a program focuses on the poorest segment of the population. This segment of population can’t provide the necessary income and expense records to prove that they qualify.

In Latin America, a popular method called Proxy Means Test (PMT) uses an algorithm to verify income qualification. 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 their homes 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.

The Inter-American Development Bank (IDB) believes that new methods beyond traditional econometrics, based on a dataset of Costa Rican household characteristics, might help improve PMT’s performance.

There are 143 columns and 9557 rows . So the dataset in itself is huge . lot of features means we need to do bit of feature Engineering.