HalimatThanni/Access-to-Healthcare-in-Nigeria

Analyzing the distribution and functionality of healthcare facilities across Nigerian states (2018–2020) # Access-to-Healthcare-in-Nigeria ## Project Overview Access to quality healthcare is one of the biggest challenges facing Nigeria’s growing population. Yet, understanding where these healthcare facilities are located, and how functional they are, remains difficult due to limited and scattered data. This project was carried out as part of the WeTech Mentorship Program, where I worked under the guidance of a mentor to explore how data analytics can reveal healthcare access gaps across regions in Nigeria. My goal was simple but powerful: *To understand how hospitals and clinics are distributed across Nigeria and how this impacts access to healthcare at the state and local government levels.* ## Problem Statement How evenly are healthcare facilities distributed across Nigeria’s states and local governments? * How many hospitals exist in each LGA and state? * How many are functional, partially functional, or non-functional? * Which categories of healthcare centers dominate (primary, secondary, tertiary)? * How many hospitals exist per 100,000 people, and how does this vary by state? ## Dataset Description Two datasets were used in this project: 1. Healthcare Facilities Dataset (2018–2020) * Contains all hospitals and healthcare centers across Nigeria’s 36 states. * Includes details on: * Facility name, LGA, and state * Category (Primary, Secondary, Tertiary) * Type (General Hospital, Clinic, Pharmacy, Laboratory, etc.) * Functional status (Functional, Not Functional, Partially Functional, Unknown) * Timestamps covering 2018 to 2020 2. Population Dataset (2016 Census Estimate) * Provides population by state, used to calculate healthcare access ratios such as hospitals per 100,000 people. ## Tools & Techniques * Power BI → Data cleaning, modeling, and dashboard creation * DAX → Time intelligence and calculated measures (e.g., hospital functionality trends, hospitals per 100k people) * Excel → Preliminary data cleaning and review * Data Modeling → Cr …