Financial KPI Reporting with PySpark & Power BI

This project processes financial transaction data using PySpark, cleans and enriches it, and prepares a dataset for visualization in Power BI.
It simulates missing amounts, calculates KPIs (cost, profit, profit margin), assigns risk segments, and outputs a clean CSV ready for business intelligence dashboards.

Features

• Automated cleaning and enrichment of messy financial data.
• Simulates missing transaction amounts for realistic KPI calculations.
• Calculates KPIs: Total Sales, Profit, Profit Margin.
• Assigns transactions to risk segments (Low, Medium, High Risk).
• Prepares the dataset for Power BI visualization with a real-world business intelligence layout.
• Ready for GitHub as a Jupyter Notebook with Markdown documentation.

Project Structure

├── Financial_KPI_Reporting.ipynb   # Main notebook
├── README.md                       # Project documentation
├── requirements.txt                # Python dependencies
├── Dashboard_image.png             # Static Power BI Dashboard image
└── Dashboard_demo.md                # Power BI dashboard demo

Installation & Setup

1. Clone this repository:

git clone https://github.com/yourusername/financial-kpi-reporting.git
cd financial-kpi-reporting

2. Create a virtual environment & install dependencies:

python -m venv venv
source venv/bin/activate   # On Windows: venv\Scripts\activate
pip install -r requirements.txt

3. Launch Jupyter Notebook:

jupyter notebook

Usage

1. Open Financial_KPI_Reporting_Cleaned_GitHub.ipynb.
2. Follow the cells step-by-step to process and clean your data.
3. Export the final dataset and load it into Power BI for visualization.

Power BI Dashboard

The prepared dataset can be visualized in Power BI to create a Financial Performance & Risk Intelligence Dashboard, featuring:

• KPI Cards: Total Sales, Profit, Avg Profit Margin, Fraud Rate
• Risk & Fraud Analysis
• Profitability & Business Drivers
• Sales Forecasting with What-If Analysis
• Transaction-Level Drilldown

Technologies Used

• Python 3.10
• PySpark
• Pandas
• Jupyter Notebook
• Power BI

Dataset & License

This project used "Financial Transactions Dataset: Analytics" dataset from Kaggle, licensed under the Apache 2.0 Dataset link: https://www.kaggle.com/datasets/computingvictor/transactions-fraud-datasets/data