JONAHKYAGABA/-Mobile-Ownership-Analysis-Ages-18-30-in-Uganda

# 📱 Mobile Ownership Analysis (Ages 18–30) in Uganda # 📱 Mobile Ownership Analysis (Ages 18–30) in Uganda This project analyzes mobile phone ownership among individuals aged 18–30 across districts, sub-counties, and parishes in Uganda. It aims to identify gender-based disparities in access to mobile technology and support data-driven inclusion efforts. --- ## 📊 Features and Visualizations ### ✅ Descriptive Statistics - Dataset overview including column types, null values, and distribution. - Gender totals and summary metrics. ### ✅ Gender-Based Insights - Pie chart of ownership by gender. - Grouped bar charts per district. - Diverging bar charts for sub-county level gaps. ### ✅ Geo-Visualizations - Choropleth maps (static and interactive) of total ownership by district. - Gender ratio maps using `GeoPandas`, `Plotly`, and `Folium`. ### ✅ Interactive Dashboards - Sunburst and treemap for hierarchical ownership breakdown. - Bubble plots showing population vs ownership with gender coloring. ### ✅ Advanced Analysis - Gender disparity ratios. - Sub-county and parish-level heatmaps. - Elbow method and KMeans clustering of districts. - "What-If" simulation: Additional phones needed to close the gender parity gap. --- ## 📁 File Structure - `mobile_ownership_analysis_final.py`: Main analysis and visualization script. - `cleaned_mobile_ownership_data.csv`: Required input dataset (not included here). - `Uganda Districts 2020.geojson`: Required for mapping Ugandan districts. - `/figs/mobile_ownership/`: Directory where output plots are saved. --- ## 🧰 Requirements Install the dependencies using pip: ```bash pip install pandas numpy matplotlib seaborn plotly geopandas scikit-learn folium