Financial Services Sales Analytics

An end-to-end data analytics project analyzing financial metrics in South Africa's financial services sector. Built using real-world structured data, Python for data cleaning, and Power BI for data visualization.

This project demonstrates data engineering, ETL, and business intelligence skills using Microsoft Azure, GitHub, and open datasets.

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Project Objectives

• Clean and analyze sales and financial performance data

• Calculate key KPIs: Profit Margin %, Operating Expense Ratio %

• Prepare data for visual analytics and dashboards

• Build an interactive Power BI report

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Tech Stack

• Python (pandas) – Data cleaning & transformation

• GitHub – Version control and portfolio hosting

• Azure Data Factory – (optional) for ETL pipelines

• Power BI – KPI dashboards and insights

• CSV files – Based on Annual Financial Statistics data from Stats SA

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Folder Structure

financial-services-sales-analytics/ ├── data/ │ ├── raw/ # Original AFS dataset │ └── processed/ # Cleaned dataset with new metrics ├── scripts/ # Python script for cleaning ├── notebooks/ # Optional EDA notebooks ├── reports/ # Power BI dashboards ├── README.md

KPI Calculations

• Profit Margin (%) = (Net Profit ÷ Revenue) × 100
• Operating Expense Ratio (%) = (Operating Expenses ÷ Revenue) × 100

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How to Run This Project

1. Clone the repo:

git clone https://github.com/ChicoSithebe/Financial-services-sales-Analytics.git 

2. Run the cleaning script

python scripts/clean_afs_data.py
03. Output file:

  •	Check data/processed/afs_2022_financial_cleaned.csv .

Power BI Dashboard
Coming soon:
• Visuals for Revenue, Profit Margin, OpEx Ratio by Industry and Year
• Top financial service sectors by performance
• Year-over-Year revenue trends
Dashboard will be added under reports/dashboard.pbix.
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License
This project is licensed under the MIT License.
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Author
Chico Sithebe
[LinkedIn Profile](https://www.linkedin.com/in/chico-sithebe)