My name is Joel Omae Okiabera. I work with the Independent Electoral and Boundaries Commission as a Constituency Elections Coordinator. My dream is to one day be the Chief Executive of a large organization. To achieve this, I'm committed to better my skills by attaining highest level possible of education. Attainment of Masters of Science in Social Statistics has been my biggest achievement so far.
Project Title:Using Random Forest to identify key determinants of Poverty in Kenya
Under the SDG’s set by the United Nations, it was estimated that all forms of poverty will be eliminated by the year 2030. Although Kenya has made tremendous improvements in poverty reduction, it is unlikely to eradicate it by the year 2030. Studies of poverty determinants in Kenya have mostly been done using classical regression methods. The world bank has suggested the use of Random Forest technique, as it is more robust in studying determinants of poverty. This study applied a random forest technique to KDHS 2014 dataset to explore poverty determinants in Kenya. The data used to analyze the critical determinants of poverty was taken from the Demographic and Health Surveys (DHS) for Kenya of 2014.The outcome variable is the wealth index categorized in five levels ranging from poorest, poorer, middle, richer and richest. The independent variables included, region, type of residence, education level, sex of the household head, marital status, number of household members and age of household head. The 2014 KDHS dataset has the wealth index of an individual coded for the five categories, while the determinant variable are both categorical and continuous. Random Forest is an algorithm used for classification and regression usually constructed from a set of classification and regression trees. The random forests are a significant improvement from classical regression techniques. Regional residence and level of education details should be considered when interventions are being made for improvements of livelihoods in the country.