Babwireyesu/A-Data-Driven-Analysis-of-Household-Access-to-Basic-Services-in-Rwanda-DHS-2020-

# A-Data-Driven-Analysis-of-Household-Access-to-Basic-Services-in-Rwanda-DHS-2020- 1. Why this topic? Because it focuses on core living conditions that affect millions of people — and it's measurable using clear, structured data. DHS 2020 includes key indicators like: Access to electricity Source of drinking water Type of toilet facilities Residence type (urban vs rural) Household wealth level Regional differences (province) As a Big Data & IT student, this topic lets you apply: Data cleaning Categorical analysis Visualization (Power BI or Python) Possibly even prediction/classification And you get to do that in a way that produces insights anyone can understand. 2. Importance of the Topic Why does it matter? These basic services are part of Sustainable Development Goals (SDGs), especially: SDG 6: Clean Water and Sanitation SDG 7: Affordable and Clean Energy Understanding these gaps helps governments, NGOs, and policymakers make smarter decisions It exposes inequality — showing which areas or groups are left behind 💬 Example: If your data shows that rural Western Province has low electricity access, that’s a signal for targeted investment. | Challenge | How to Overcome It | | ----------------------------------------- | ---------------------------------------------------------- | | DHS dataset is large and complex | Focus only on a few key variables (electricity, water) | | Data is coded numerically (e.g., 1 = Yes) | Use the DHS codebook to decode variables | | Missing or inconsistent data | Use Python or Power BI to filter, clean, and impute | | Urban-rural or regional imbalance in data | Use grouping and percentage analysis instead of raw counts | ## Project Overview Analysis of electricity, water, sanitation, and internet disparities using Rwanda DHS 2020 data. ## Tools Used - Python (Pandas for cleaning, Jupyter Notebook) …