Road Safety Analysis | Python, SQL & Power BI

Developed an end-to-end road safety analytics project to help authorities understand accident patterns, identify high-risk areas, and make informed decisions about road safety interventions.

The project involved cleaning and preparing road accident data using Python and Pandas, followed by SQL-based exploratory analysis to investigate accident trends, casualty figures, severity distributions, and geographical patterns.

Using Power BI, I transformed the findings into an interactive dashboard featuring key performance indicators and visualisations that provide an overview of accident activity, highlight trends over time, and reveal differences in accident occurrence across districts and locations.

The analysis also explored accident severity in relation to road types, weather conditions, lighting, and road surface conditions.

Tools used: Python, Pandas, SQL, Power BI.

Project deliverable: An interactive Power BI dashboard supported by data cleaning, transformation, and SQL analysis.

The objective was to turn raw accident data into clear, meaningful insights that can support evidence-based road safety planning and resource prioritisation.

🚀 Take a look at our newly upgraded Enterprise POS architecture in action! We completely re-engineered this system, migrating from a Python CustomTkinter GUI to a lightning-fast Tauri front-end paired with a robust Python FastAPI backend.

🔄 The Architecture Upgrade: CustomTkinter to Tauri

Modern Web UI: We stripped away the rigid CustomTkinter framework and rebuilt the front-end using standard HTML, CSS, and vanilla JavaScript. This shift allows for pixel-perfect styling, fluid animations, and a much cleaner, responsive layout.

Decoupled FastAPI Backend: Instead of weaving the UI and database logic together, Python now acts as a standalone, high-performance API. The FastAPI backend handles all the heavy SQLite database operations, keeping the application incredibly snappy.

Seamless API Communication: The front-end and back-end now communicate flawlessly via local HTTP endpoints, allowing the UI to fetch live inventory and process checkouts asynchronously.

The Tauri Magic: Tauri wraps this entire modern web stack into a highly optimized, low-memory native desktop executable, automatically spawning the Python backend as a silent background sidecar process.

🛒 Live Sales Terminal: Watch how seamlessly the checkout flow operates. Users can quickly select products from a searchable catalog, append them to the live billing ledger, and instantly complete transactions with zero lag.

📦 Master Inventory Manager: Total control over stock is just a few clicks away. We showcase the live adjustment capabilities by selecting an item in the manual correction terminal, inputting an intervention magnitude of 55 units, and securely updating the database through a clean confirmation modal.

📊 Deep Analytics & Logs: Data drives decisions. The walkthrough highlights the comprehensive transaction ledger tracking real-time daily metrics—like total revenue, transaction counts, and top-selling products—alongside a detailed, modal-based Monthly Sales Summary.

⚡ This project proves that robust, localized SQLite databases and powerful Python backends can be perfectly paired with beautifully sleek, modern web-based user interfaces.