Project IV: Classification and Targeting of Vulnerable Households in Mauritania by Socio-demographic Category and Consumer Spending
- Problem:
The main challenge lies in developing an accurate and reliable model capable of classifying households as poor or non-poor based on socio-demographic variables and consumer spending data. This classification is essential for devising targeted policies aimed at efficiently allocating resources and social assistance programs.
- Proposed Solution:
This machine learning project aims to develop a robust classification model using logistic regression techniques to predict household poverty status based on EPCVM data. By applying the classic steps of a machine learning project, including data preparation, feature selection, model construction, evaluation, and interpretation of results, we aim to create a reliable tool to identify households in poverty.
