Welcome to my portfolio! Here, you'll find my data analysis projects covering various domains, including road accidents, education statistics, sales analytics, and financial analysis. Each project is designed to showcase my skills in data cleaning, visualization, and insights generation using tools like Excel, Power BI, Python, and SQL.
Overview: This project analyzes road accident data to uncover trends, severity levels, and key risk factors. Using Microsoft Excel, I created an interactive dashboard to visualize accident patterns and draw insights.
- 266,492 total casualties recorded
- Cars contribute 79.8% of accidents
- Most accidents occur on single carriageways (196,086 cases)
- 66.25% of accidents happen in rural areas
- Wet road surfaces contribute significantly to accidents
Overview: This Power BI dashboard provides an analytical view of the Australian education sector, focusing on schools, students, and staff distribution. It explores key metrics such as school numbers by sector, student enrollments, and staff allocation across different states and territories over the years.
- Total Schools: 66,000 (Government: 70.79%, Catholic: 18.18%, Independent: 10.91%)
- New South Wales has the highest number of schools (21,590)
- Total Students: 51,543,267 (Government: 65.32%, Catholic: 20.36%, Independent: 14.32%)
- Student-to-Staff Ratio: 27.69
- The Government sector employs the highest percentage of staff
Overview: This Power BI dashboard provides an in-depth analysis of annual sales performance, tracking revenue, order volume, and customer insights. The dashboard visualizes sales trends over time, revenue distribution by product category, color, and gender, as well as key customer analytics.
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💰 Total Revenue: $29.3M
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📦 Total Orders: 91,321
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🛒 Average Order Value (AOV): $320.9
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📈 Revenue & Orders Growth: Consistent increase from 2014 to 2016, with a major spike in Q4 2016.
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🚴 Bikes contribute 96.62% ($28M) of revenue, while Accessories & Clothing contribute only 2.28% ($1M).
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🎨 Top Selling Colors: Black ($8.8M), Red ($7.7M), Silver ($5.1M) – Blue has the lowest revenue ($2.3M).
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👨💼 Revenue by Gender: Males (50.46%) and Females (49.54%) contribute almost equally.
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🏆 Top Customer (Morgan): $144.7K in total sales and 435 orders.
Overview: This Power BI dashboard provides an in-depth analysis of incidents resulting in fatalities and injuries. The dataset highlights the primary causes, severity, and geographical distribution of casualties.
Total Fatalities: 2,405,879
Total Injuries: 303,179
Death Rate: 89%
Most Deadly Incident Types: Airstrikes (0.88M deaths), Building Collapse (0.56M deaths), Missile Strikes (0.33M deaths)
Most Injuries Caused By: Building Collapse (130K injuries), Missile Strikes (82K injuries)
85.31% of fatalities are due to direct conflict & lack of medical access
High-Risk Cities: Gaza City (338K deaths), Deir al-Balah (79K deaths), North Gaza (79K deaths)
🚀 Always open to collaboration and learning!