Welcome to my first-ever data analytics project, where I manually collected and analyzed invoice data for my company (name withheld). This project was driven by a real-world challenge: understanding the spending and billing trends that the company was receiving from the office park where we were renting. My boss wanted clarity on how we were being billed, so I took on the task of transforming unstructured invoice data into actionable financial insights.
This project involved converting EML email files (containing invoices) into PDFs, extracting the relevant financial data manually, and entering it into a structured spreadsheet. Once compiled, I performed:
✔ Data Cleaning – Removing inconsistencies and ensuring accuracy
✔ Sorting & Grouping – Categorizing expenses for analysis
✔ Visualization & Trend Identification – Creating clear graphs to showcase spending patterns
The final report tracks monthly expenses across categories such as:
🔹 Electricity Usage – Understanding peak months and efficiency shifts
🔹 Solar Energy – Tracking sustainability and cost reductions
🔹 Rent & Levies – Studying consistency and adjustments
🔹 Parking & Training Costs – Identifying stable vs. fluctuating expenses
This hands-on process introduced me to data transformation, organization, and visualization, making it a great learning experience in data analytics.
- 🐍 Python – Data manipulation (Pandas & NumPy)
- 📊 Matplotlib & Seaborn – Expense trend visualization
- 🏗 Google Colab – Notebook coding environment
- 💾 CSV & Excel – Manual data entry & structuring
- ✉ EML to PDF Conversion – Extracting invoices from emails
- 🚀 GitHub – Version control & project sharing
🔹 Electricity expenses peaked in September 2024, highlighting seasonal demand changes.
🔹 Solar energy costs steadily declined, indicating improved efficiency.
🔹 Rent remained stable, but a notable increase occurred in March 2025.
🔹 Generator & Diesel usage were minimal, reflecting effective backup strategies.
Through this analysis, I developed a better understanding of financial trends, how to clean messy data, and the importance of visual storytelling in analytics.