mahmoud-47/infant-mortality-prediction-ssa

This repository contains the code and materials from a hackathon project focused on addressing infant mortality in Sub-Saharan Africa (SSA). # Infant Mortality Prediction Model for Sub-Saharan Africa This repository contains the code and materials from a **hackathon project** focused on addressing **infant mortality** in **Sub-Saharan Africa (SSA)**. The goal of this project was to develop a **data-driven model** that helps **local governments** predict infant mortality rates based on key health metrics, such as **vaccination rates**, **healthcare coverage**, **prematurity**, and **birth asphyxia**. The model aims to assist governments in assessing the potential outcomes of their planned actions, like healthcare interventions and vaccination campaigns, and to support better decision-making for improving infant survival rates. ## **Key Features:** - **Data-driven model** to predict infant mortality based on healthcare-related factors. - **Correlation analysis** to identify the most influential health indicators, such as vaccines and skilled birth attendance. - **Decision-making tool** to help governments assess the impact of their actions on infant mortality. ## **Technologies Used:** - **Programming Language**: Python - **Libraries**: - Pandas - NumPy - Scikit-learn - Matplotlib - **Data Sources**: Various health and mortality reports for SSA countries ## **Project Structure:** - `code.ipynb`: The main Python script that contains the model and functions for making predictions. - `data/`: A folder containing sample data files used for training and testing the model. - `presentation.pdf`: The presentation slide deck summarizing the project and results. ## **How to Use:** 1. **Clone the repository:** ```bash git clone github.com cd infant-mortality-prediction-ssa