Child Mortality prediction using Machine Learning Techniques

Children under the age of five are considered vulnerable in this context.The under-five mortality rate, or the death rate for children under the age of five, refers to the probability of dying between birth and five years of age.The death of a fetus is as common as that of a child.The objective is to explore AI-driven methods for determining the most accurate fetal health mortality prediction model.A comprehensive analysis of the entire dataset will be conducted using the supervised machine learning technique (SMLT) to identify key data points, including variable identification, univariate analysis, bivariate analysis, and multivariate analysis, along with addressing missing data, data validation, cleaning, preprocessing, and visualization.Based on the findings of this research, a holistic approach for performing sensitivity analysis on model parameters influencing fetal health classification has been developed.This project proposes a machine learning-based framework for predicting child mortality and evaluates various machine learning techniques against the provided dataset