An early portfolio project demonstrating data exploration, KPI calculation, and analytical storytelling using Microsoft Excel.
The goal of this project was to analyse a global road traffic accidents dataset in order to identify key factors linked to accident frequency and severity, and to present the results in an interactive Excel dashboard.
This analysis focuses on understanding accident risk factors to support data-driven safety and prevention discussions.
Global road traffic accidents dataset (10,000 records), sourced from Kaggle.
- Structured raw data into an Excel table for analysis
- Built core KPIs:
- Total accidents
- Total casualties
- Average vehicles involved
- Average casualties per accident
- Used PivotTables and PivotCharts to analyse:
- Weather conditions
- Accident causes
- Locations
- Time trends (monthly and quarterly)
- Created an interactive dashboard using slicers (location, month, weather)
- Wrote an executive summary with data-backed insights and recommendations
- Accident causes are evenly distributed, with no single dominant risk factor.
- Clear weather conditions still show the highest accident volume, suggesting risky behaviour plays a major role.
- England records the highest total number of casualties, indicating location-based concentration of risk.
- Accident volume remains relatively stable over time, with only minor seasonal variation.
- Microsoft Excel (tables, PivotTables, PivotCharts, slicers)
- Data structuring and cleaning in Excel
- KPI definition and validation
- PivotTable-based analysis
- Trend and pattern identification
- Analytical storytelling
- Dashboard design for non-technical users
- Excel dashboard file
- Dashboard and summary screenshots
Example PivotTable and PivotChart used to analyse accident volume by weather condition.
Note: The dataset represents a synthetic global sample and is used for analytical demonstration purposes.