AdamsAghoghoEvarister/Malaria-Incidence-Trends-Nigeria

Data analysis project tracking WHO malaria incidence trends in Nigeria (2000–2023) using Power Query, Excel, and interactive Power BI visualizations. # Malaria Incidence Trends in Nigeria (2000–2023) An end-to-end data analysis project investigating World Health Organization (WHO) malaria incidence trends in Nigeria from 2000 through 2023. This repository documents the entire workflow—from raw data extraction and Power Query transformation to DAX modeling and interactive Power BI dashboard visualization. --- ## 📌 Project Overview Malaria remains a major public health concern in Sub-Saharan Africa. This project analyzes historical WHO data for Nigeria to evaluate long-term incidence trends, track key performance indicators (KPIs), and highlight progress made in malaria control over a 23-year period. ### Key Objectives * **Trend Analysis:** Identify temporal shifts in malaria incidence per 1,000 population at risk. * **Data Cleaning & Transformation:** Handle missing values, standardize columns, and format data types using Power Query. * **Interactive Visualization:** Build an intuitive Power BI dashboard to communicate findings to stakeholders clearly. --- ## 🛠️ Tools & Technologies * **Microsoft Excel / Power Query:** Data cleaning, conditional column creation, missing value handling, and structural transformations. * **Power BI:** DAX measures, data modeling, KPI card design, and interactive visualization. * **GitHub:** Version control, documentation, and portfolio hosting. --- ## 📊 Key Findings & Insights * **Overall Trend:** Analyzed multi-decade WHO incidence figures to identify peak transmission periods and multi-year decline patterns. * **Impact Assessment:** Identified notable reduction milestones corresponding with national public health intervention rollouts. * **Data Consistency:** Standardized historical annual reporting metrics to ensure accurate year-over-year comparative analysis. --- ## 📁 Repository Structure ```text ├── 2_ Raw Dataset.csv # Original uncleaned WHO dataset ├── 3_ Cleaned dataset.xlsx # Processed data following Power Query transformations ├── …