• Data Scope: Analyzed ~5,000 customer transaction records to uncover purchasing patterns.

• Data Cleaning (Python): Performed imputation & category mapping to ensure integrity for business logic.

• SQL Insights: Executed complex queries identifying that subscribers spend 64% more than non-subscribers.

• Market Strategy: Pinpointed the 51+ age group as the highest revenue-generating segment.

• Category Dominance: Electronics identified as the top revenue driver, informing inventory priority.

• Discount Impact: Validated that discount campaigns increased average purchase value from $167 to $202.

• Customer Loyalty: Segmented 5,000 users into New, Returning, and Loyal groups for retention.

• Seasonal Growth: Found Winter, Summer, & Spring seasons drive 73% of annual revenue.

• Visualization: Built an interactive Power BI dashboard tracking KPIs like Total Spend ($886K).

• Actionable Results: Optimized payment methods and shipping strategies based on data-backed trends.

• Core Objective: Conducted a comprehensive analysis of 307K+ accident records to identify safety critical points.

• KPI Tracking: Developed metrics for Total Casualties (418K+) and categorized them by severity (Fatal, Serious, Slight).

• Vehicle Analysis: Identified that 'Cars' account for the highest percentage (79.8%) of total casualties.

• Temporal Trends: Mapped monthly casualty trends, showing a significant peak in November year-over-year.

• Road Conditions: Analyzed the impact of road types, finding 'Single Carriageway' as the highest risk zone (74%).

• Light & Weather: Discovered that 73% of accidents occur during daylight and 80% in dry conditions.

• Urban vs Rural: Segmented data to show Urban areas lead in accident frequency at 62%.

• Advanced DAX: Used complex DAX measures to calculate Year-on-Year (YoY) growth and dynamic filtering.

• Map Integration: Integrated geographic maps to visualize accident hotspots across different regions.

• Strategic Insights: Provided data-driven recommendations for traffic management and emergency response planning.

• Data Scale: Processed a transactional dataset tracking over 48K+ individual pizza sales entries to discover commercial trends.

• KPI Analysis: Evaluated business performance metrics, discovering a Total Revenue of $817.8K+ across 21,350 total orders.

• Sales Volumetrics: Analyzed the distribution of 49,574 total pizzas sold, with an Average Order Value calculated at $38.30.

• Temporal Order Patters: Pinpointed Friday as the highest sales traffic day (3,538 orders) and identified peak hourly demand from 12:00 PM to 1:00 PM and 5:00 PM to 6:00 PM.

• Category Distribution: Tracked revenue shares across categories, identifying the 'Classic' pizza segment as the major driver bringing in 26.9% of total sales.

• Size and Inventory Metrics: Determined that 'Large' sized pizzas heavily dominate consumer preference, contributing 45.8% to overall revenue generation.

• Product Performance: Identified 'The Classic Deluxe Pizza' as the best seller by volume (2,453 sold) and 'The Brie Carre Pizza' as the lowest volume contributor (490 sold).

• Business Intelligence: Extracted order throughput metrics revealing an average unit load of 2.32 pizzas per customer transaction to optimize staff scheduling and inventory preparation.