📘 Nigerian Exchange Rate Scraper & Dashboard

📌 Project Title

Nigerian Exchange Rate Analysis using Web Scraping and Power BI

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📖 Description

This project demonstrates how to fetch daily foreign exchange rates from the official Central Bank of Nigeria (CBN) website using a Python script, save the data into a CSV file, and create interactive visualizations in Power BI.

It’s a practical application of web scraping using APIs, combined with business intelligence reporting.

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🧪 How It Works

1. Python Web Scraping

   • Uses the requests library to extract exchange rate data from the CBN API.
   • Converts the JSON data to a DataFrame using pandas.
   • Saves the cleaned data to Nigeria_Exchange_Rate.csv.

2. Power BI Visualization

   • Imports the CSV file into Power BI.
   • Visualizes exchange rates for multiple currencies over time.
   • Enables date-based comparison of rates such as USD, Euro, Pound Sterling, CFA, Yen, and more.

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🔧 Technologies Used

• Python (requests, pandas)
• Jupyter Notebook
• Power BI Desktop
• CBN Exchange Rate API
  https://www.cbn.gov.ng/api/GetAllExchangeRatesGRAPH

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📂 Project Structure

File Name	Description
Web Scrapping using Api.pdf	Contains the Python code to fetch and save exchange rate data
Web Scrapping using Api Visuals.pdf	Screenshots and output from Power BI dashboard
Nigeria_Exchange_Rate.csv	Output CSV file generated by the Python script (not included by default)

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🖼️ Power BI Dashboard Highlights

• 📆 Daily exchange rate trends
• 📊 Multiple currency tracking (USD, EUR, GBP, CFA, YEN, etc.)
• 📈 Visual comparison over different dates
• 🔍 Easy-to-use interface for exploration

Note: This dashboard is a conceptual learning project, not an official financial tool.

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🚀 How to Run This Project

1. Clone the repository

   git clone https://github.com/Oluwakoya-ao/nigerian-exchange-rate-analysis.git

2. Run the Python script (from Jupyter Notebook or terminal):

   # In a Jupyter cell or Python script
   fetch_and_save_data()

   This will save a file called Nigeria_Exchange_Rate.csv.

3. Open Power BI Desktop, import the CSV, and recreate the visuals.

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🧠 Learning Goals

• Understand how to extract live data using APIs
• Practice data handling and transformation using Python
• Build professional dashboards using Power BI

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🙏 Credits & Acknowledgment

This project was developed as part of the Data Analytics – Deep Dive Mentorship Program.
Special thanks to the original creator of the project, who kindly granted permission to replicate and learn from their work.

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✍️ Author

Oluwakoya Oluwafemi
Data Analyst | Power BI Developer
📧 oluwakoyafavour@gmail.com
🔗 LinkedIn Profile