ifexxx-dat/Maternal-And-Child-Mortality-Analysis-in-Nigeria-2013-2023-..

2 Page Power BI dashboard analyzing 10 years of maternal & child health data across Nigeria (2013-2023). Explores trends, geographic disparities, and intervention effectiveness. Built with DAX, Power Query, and healthcare analytics insights. Nigerian Maternal And Child Health Dashboard (2013-2023) Project Overview This project analyzes 10 years of maternal and child health data across Nigeria’s 37 states to track progress, identify disparities, and evaluate the effectiveness of healthcare interventions. Using Power BI, I built a two-page interactive dashboard that tells the story of Nigeria’s healthcare journey from 2013 to 2023 the wins, the gaps, and where urgent action is needed. The Core Question: Has Nigeria made meaningful progress in maternal and child health over the past decade, and who is being left behind? Dataset Source: Synthetically generated dataset based on real Nigeria health trends and patterns Reference Data: WHO Global Health Observatory, UNICEF, Nigeria Demographic and Health Survey (NDHS) Coverage: All 37 states (36 states + FCT) across 5 survey years (2013, 2015, 2018, 2021, 2023) Tables: 2 CSV files Health Indicators Table (~925 rows, 14 columns) Population Data Table (185 rows, 5 columns) Health Indicators Tracked: - Under-5 mortality rate, infant mortality rate, neonatal mortality rate - Maternal mortality ratio - Vaccination coverage (DPT3 and Measles) - Skilled birth attendance and antenatal care (4+ visits) - Stunting, wasting, underweight prevalence - Exclusive breastfeeding rates Data Quality Issues (Intentional for cleaning practice): - Inconsistent state name formatting (“Lagos” vs “LAGOS” vs “Lagos State”) - 8% missing values in mortality columns - Population figures in thousands (required scaling) - Duplicate rows Tools & Technologies - Power BI Desktop - Dashboard development and visualization - Power Query (M) - Data cleaning and transformation - DAX - Calculated measures and analytics Project Workflow 1. Data Cleaning (Power Query) - Standardized inconsistent state names using Capitalize Each Word and Replace Values - Removed ~8% null rows from mortality columns - Multiplied all population columns by 1,000 to correct data scale - Removed duplicate …