Classifying Fetal health with Machine Learning
A comparative study of linear regression and decision tree to predict the child mortality rate based on other socioeconomic indicators from the World Bank.
This project involves exploratory analysis on data (child mortality rate) using Python. Then, creating a presentation with explanatory plots to communicate findings. (Udacity Project)
Which African countries beat the child-mortality trend and why? UN IGME/World Bank panel analysis: SDG 3.2 pace, beat-the-trend residuals, and an honest ML-vs-baseline comparison.
World Bank economic data combined with UNICEF child welfare indicators (mortality, nutrition, poverty)
Australia SA3-level analysis of relationship between child mortality/morbidity and climate conditions/air pollution
This project analyzes child mortality rate trends across countries and income groups from 1990 to 2023. It uses data sourced from Our World in Data, cleansed for consistency, and visualized using Python in Google Colab. The goal is to uncover patterns, highlight disparities, and promote awareness of global child health issues.
Determining the most important predictors of diarrhoea in children under five in South and Southeast Asia by exploring the spatiotemporal association between diarrhoeal incidence and various behavioural, socio-demographic, and environmental factors.
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