Team: Survival at Birth: Analyzing Maternal and Child Mortality Across the Globe
Background:
Maternal and child mortality rates vary significantly across different regions of the world. Understanding the key factors influencing these disparities—such as climate conditions, economic status, and healthcare accessibility—can provide valuable insights into public health challenges and potential interventions.
Project Objectives:
• Analyze global maternal and child mortality rates.
• Identify correlations between mortality and factors such as climate, average income, and healthcare access.
• Compare regional differences and assess potential patterns in the data.
• Provide data-driven insights that could inform policy recommendations.
Data Sources:
• UNICEF (United Nations International Children’s Emergency Fund)
• WHO (World Health Organization)
Methodology:
• Train predictive models (e.g., Random Forest, XGBoost, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), or Logistic Regression) to estimate maternal and child mortality rates based on regional factors.
• Use unsupervised learning techniques (e.g., K-Means Clustering, Hierarchical Clustering) to uncover hidden patterns and classify regions with similar mortality trends.
• Apply feature importance analysis to determine which factors have the most significant influence on maternal and child mortality rates