A Comparison of Machine Learning and Deep Learning Models for Predicting Household Food Security Status

ML and DL algorithms are becoming more popular to predict household food security status, which can be used by the governments policymakers of country provide a supply for needy in case emergency. models, namely: k-Nearest Neighbor (kNN), Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) Convolutional network (CNN) investigated status Household Income, Consumption Expenditure (HICE) survey data Ethiopia. The standard evaluation measures such as accuracy, precision, recall, F1-score, Mean Absolute Error (MAE), Root Square (RMSE) evaluate models' predictive performance, experimental results reveal that ANN, model surpassed classifiers with an accuracy 99.15%