Abstract
The study used supervised machine learning approaches to predict infant mortality in Kenya and
the 2014 Kenya Demographic and Health Survey. Di erent classi cation methods were used. The
methods were Logistic regression, K-nearest neighbor and Random forest model. Random Forest
performed well with an accuracy of approximately 97.1% followed by Logistic Regression model
with 86.1% and K-nearest neighbor with 85.6%. The results concluded that random forest model
was the best performing model.