Faith Musili holds an MSc. in Social statistics from the University of Nairobi and a BSc. in Geomatic Engineering and G.I.S from Dedan Kimathi University of Technology. Her education combines remote sensing, GIS and data science skills set. She is passionate about data science, machine learning, spatial modelling, shiny dashboard development, spatial mapping and remote sensing. She is also a R programming and open source software’s enthusiast. She is a co-organizer of R-Ladies Nairobi chapter which aims to bring diversity and create a networking platform for R users in Nairobi. Previously, Faith was a Junior data scientist and developer at the World Agroforestry center (ICRAF). Currently, she is a Senior data analyst at the Norwegian Refugee Council (NRC) regional office in Nairobi. Her masters project was on poverty-based classification of households using K-means and K-medoid clustering algorithms under the guidance of Dr. Timothy Kamanu.
Project Title:Poverty-based classification of households using cluster analysis.
Poverty in rural areas is complex and multi-dimensional. Most of the poor households in Sub-Saharan Africa (SSA) rely on agriculture for livelihood. Agri-climatic shocks such as prolonged droughts, outbreak of animal and human diseases and crop and pest diseases make rural poor households in SSA vulnerable. Research gaps exist on poverty-based clusters in Kenya rural areas. The clusters would be fundamental in understanding the determinants of poverty. This study uses K-means and K-medoid algorithms to identify poverty-based clusters in Kenya rural areas. The data used is collected from rural farming households. K-means and K-medoid algorithms are the most common clustering algorithms used and have been implemented by researchers. The results show that rural poor households have low education level, high dependency ratio, low gender parity ratio, low income and low household diet diversity compared to rural non-poor households. Rural non-poor households own agricultural productive assets, seek extension services, are more aware of financial services and products available to farmers and access financial services more compared to rural poor households. Knowledge on the determinants of poverty in Kenya rural areas can be used by the government, institutions and partners, to formulate strategies and policies in an effort to reduce poverty. In future, research should be conducted on the role of land sizes and land tenure on poverty in rural Kenya.