matzolla/3rd-prize-winner-Microsoft_AIMS-DataScience_hackathon

About the Problem Granular information on economic well being is extremely useful for governments and policy makers. But household surveys that capture this kind of information are expensive and conducted infrequently in many African countries. For this competition we will attempt to create a workaround for this lack of data by building a model able to predict a measure of wealth as measured in household surveys using readily available inputs. Using data from 18 different countries collected at different times, you must correctly predict the cluster-level estimated wealth measures found from surveys in 7 different countries not covered in the training data. A successful model could be useful for filling in the gaps between the more expensive surveys. # $3^{rd}$ prize-winner: ### Microsoft AIMS-DataScience hackathon `About the Problem Granular information on economic well being is extremely useful for governments and policy makers. But household surveys that capture this kind of information are expensive and conducted infrequently in many African countries. For this competition we will attempt to create a workaround for this lack of data by building a model able to predict a measure of wealth as measured in household surveys using readily available inputs. Using data from 18 different countries collected at different times, you must correctly predict the cluster-level estimated wealth measures found from surveys in 7 different countries not covered in the training data. A successful model could be useful for filling in the gaps between the more expensive surveys.`