Identifying Risk Factors and Predicting Food Security Status Using Supervised Machine Learning Techniques

dc.contributor.author	ALELIGN, MELAKU
dc.date.accessioned	2024-02-12T07:36:26Z
dc.date.available	2024-02-12T07:36:26Z
dc.date.issued	2024-02-12
dc.identifier.uri	http://hdl.handle.net/123456789/7188
dc.description.abstract	The findings of this study show that the most risk factors and the best appropriate supervised machine learning algorithm for predicting the food security status of the household heads identified. Risk factors became statistically significant at p<0.05. So, Intervention strategies should emphasize on risk factors and important rules as well as the C4.5 algorithm to improve the food security status of the household head	en_US
dc.description.sponsorship	uog	en_US
dc.language.iso	en_US	en_US
dc.subject	The findings of this study show that the most risk factors and the best appropriate supervised machine learning algorithm for predicting the food security status of the household heads identified. Risk factors became statistically significant at p<0.05. So, Intervention strategies should emphasize on risk factors and important rules as well as the C4.5 algorithm to improve the food security status of the household head	en_US
dc.title	Identifying Risk Factors and Predicting Food Security Status Using Supervised Machine Learning Techniques	en_US
dc.type	Thesis	en_US