josh-ai2/Africa-Infrastructure-Explorer

Visualizing infrastructure gaps in piped water and electricity access across Africa using Afrobarometer survey data and predictive modeling # Africa-Infrastructure-Explorer **Visualizing infrastructure gaps in piped water and electricity access across Africa using Afrobarometer survey data and predictive modeling.** ## Overview This project aims to visualize critical aspects of infrastructure and essential services across Africa using data from the Afrobarometer Round 7 and Round 8 surveys. The focus is on mapping areas with piped water access, sewage systems, electricity grids, and cell service. Additionally, a Random Forest model is employed to predict piped water access based on various survey variables. Dataset Mapping Visualizaton ## Features - **Geospatial Visualizations**: Interactive maps displaying piped water access, electricity grid availability, sewage systems, and cell service across Africa. - **Infrastructure Gaps**: Identifies regions lacking essential services using visual markers. - **Random Forest Predictions**: A machine learning model that predicts piped water access based on survey responses. ## Data Source The data used in this project is sourced from the Afrobarometer survey network, specifically Rounds 7 (2017) and 8 (2019). Afrobarometer collects public opinion data on democracy, governance, the economy, and social issues across Africa. - **Afrobarometer Round 7**: 45,826 entries - **Afrobarometer Round 8**: 48,088 entries ## Methodology ### Pre-Visualization Data Cleaning 1. Retained only relevant columns (piped water access, electricity grid presence, sewage system, and cell service). 2. Removed indecision responses (-1 and 9 values), filtering approximately 900 rows. 3. Extracted geospatial data (latitude, longitude) for heatmap generation. ### Random Forest Model - Focused on predicting piped water access, as there were no reliable predictors for electricity grid availability. - The model's accuracy improved from **0.7627** in Round 7 to **0.7954** in Round 8. - The most significant predictors were: - **Sewage System Availability** - **Water Source Location** …