victorumeh/Africa_health_care_project_excel_python_powerBI

Africa Healthcare Analytics — End-to-end data project analysing disease burden, patient outcomes & gender distribution across 10 African countries. Cleaned with Python & Excel, visualized in Power BI. Covers ~13K+ patient records across 10 diseases, 4 regions & 5 age groups. # 🏥 Africa Healthcare Project > **An end-to-end healthcare data analytics project** covering disease burden, patient outcomes, gender distribution, and immunisation status across 10 African countries — cleaned with Python & Excel, visualised in Power BI. --- ## 📌 Project Overview This project analyses healthcare data across **10 African nations** to support evidence-based decision-making for healthcare professionals, policy advisors, and programme managers. The pipeline runs from raw data through Python-based cleaning, Excel validation, and finally a fully interactive Power BI dashboard. | Metric | Value | |---|---| | 🌍 Countries | 10 | | 🦠 Diseases Tracked | 10 | | 👥 Patient Records | ~13,000+ | | 🗺️ Regions | 4 (East, North, Southern, West Africa) | | 📊 Outcome Categories | 4 (Deceased, Recovered, Referred, Under Treatment) | | 👶 Age Groups | 5 (0–18, 19–35, 36–50, 51–65, 65+) | --- ## 🗂️ Repository Structure ``` africa-healthcare-project/ │ ├── data/ │ └── Cleaned_Africa_Healthcare_Project.xlsx # Final cleaned dataset │ ├── notebooks/ │ └── africa_healthcare_python_cleanup.ipynb # Python data cleaning notebook │ ├── dashboard/ │ └── africa_health_care_PBI.pbix # Power BI dashboard file │ ├── reports/ │ └── Africa_Healthcare_Stakeholder_Report.docx │ └── README.md ``` --- ## 🛠️ Tech Stack | Tool | Purpose | |---|---| | 🐍 Python (Jupyter Notebook) | Data cleaning, deduplication, standardisation | | 📊 Microsoft Excel | Validation, column engineering, final dataset prep | | 📈 Microsoft Power BI | Interactive dashboard & visualisations | | 📝 Word / Markdown | Stakeholder reporting | --- ## 🔄 Data Pipeline ``` Raw Dataset │ ▼ [Stage 1] Python Cleaning (Jupyter Notebook) • Remove duplicates • Standardise categories (disease, country, gender, outcome) • Handle missing values • Validate date fields (Admission_Date) │ ▼ [Stage 2] Excel Validation • Conditional formatting for outlier detection • Age group banding & regional classificat …