KingDavid9999/africa-economic-indicators-excel-dashboard

Interactive Excel dashboard analysing GDP, inflation, unemployment and FDI across 54 African countries (2000–2014) using World Bank data, Power Query and Pivot Charts. # Africa Economic Indicators Dashboard An interactive Microsoft Excel dashboard analysing key economic indicators across 54 African countries from 2000 to 2014, built using World Bank data. --- ## Dashboard Preview --- ## Project Overview This project was built to analyse and visualise economic trends across Africa using real-world World Bank data. The goal was to answer key business questions about regional economic performance, inflation, unemployment, and foreign investment — with a dedicated spotlight on Nigeria's economic trajectory over two decades. The project demonstrates the full data analyst workflow: sourcing raw data, cleaning and transforming it, building analytical pivot tables, and presenting insights through an interactive dashboard. --- ## Tools Used | Tool | Purpose | |------|---------| | Microsoft Excel (2021) | Primary analysis and dashboard tool | | Power Query | Data loading, filtering and transformation | | Pivot Tables & Pivot Charts | Data aggregation and visualisation | | XLOOKUP | Cross-sheet data mapping | | Excel Slicers | Interactive dashboard filtering | --- ## Data Source - **Source:** World Bank World Development Indicators - **Platform:** Kaggle — World Development Indicators Dataset - **Coverage:** 54 African countries, 2000–2014 - **Indicators Used:** - GDP per capita (current US$) — `NY.GDP.PCAP.CD` - Inflation, consumer prices (annual %) — `FP.CPI.TOTL.ZG` - Unemployment, total (% of total labour force) — `SL.UEM.TOTL.ZS` - Foreign direct investment, net inflows (% of GDP) — `BX.KLT.DINV.WD.GD.ZS` - Population growth (annual %) — `SP.POP.GROW` --- ## Data Cleaning & Preparation The raw dataset (547MB) required significant cleaning and transformation before analysis: - **Power Query filtering** — loaded the 547MB Indicators.csv file into Power Query and filtered it down to African countries only using an Inner Join merge against a Country reference file, reducing the dataset from 4+ million rows to ~3,800 rows …