Costa Rican Household Poverty Level Prediction哥斯达黎加家庭贫困水平预测

为了改善PMT，IDB（拉丁美洲和加勒比海地区最大的发展筹资来源）已转向Kaggle社区。他们认为，基于哥斯达黎加家庭特征数据集的传统计量经济学以外的新方法可能有助于改善PMT的性能。

除哥斯达黎加以外，许多国家都面临着同样的问题，即无法准确地评估社会需求。如果Kagglers可以产生改进，则新算法可以在世界其他国家/地区实施。

To improve PMT, the IDB (the largest development financing source in Latin America and the Caribbean) has turned to the Kaggle community. They argue that new methods beyond traditional econometrics based on Costa Rica's household characteristic dataset could help enhance the performance of PMT. Apart from Costa Rica, many countries face the same problem of being unable to accurately assess social needs. If Kagglers can deliver improvements, the new algorithm can be implemented in other countries around the world.

搜集汇总

背景与挑战

背景概述

该数据集由美洲开发银行发起，旨在通过Kaggle社区改进代理手段测试方法，以更准确地预测哥斯达黎加家庭的贫困水平。数据集包含142个特征，涉及家庭属性与人口统计信息，目标变量为四类贫困等级分类。