This project is based on pizza sales data. The assumed clients want a sales data dashboard for Jan 15th to Dec 25th. By analyzing various aspects of the data, contains data about accidents occurring with different influences like time, location or vehicle, we seek to identify trends, and make visualization to enable easier and adequate comprehension.

The primary Data set used for this analysis is "pizza_sales_excel_file", containing details about sales.

SQL - Data cleaning, analysis, KPI creation and formatting.

POWER BI - Data transformation and visualization.

In the initial data preparation phase, we performed the following tasks:

Data Loading and inspection.

Data cleaning and formatting.

Two columns for day and month were created using Data transformation in Power BI.

Searched and removed any duplicate and void data.

calculate total revenue

Average value order

sum of total orders

Average pizza per order

Top 5 pizza by revenue

Least 5 pizza by revenue

'''SQL select Top 5 pizza_name, sum(total_price) as total_revenue from pizza_sales group by pizza_name order by Total_revenue desc

'''SQL select Top 5 pizza_name, sum(total_price) as total_revenue from pizza_sales group by pizza_name order by Total_revenue asc

'''SQL SELECT SUM(TOTAL_PRICE) AS Total_revenue FROM pizza_sales several KPIs were created for projection of the visualization

The analysis result are summarized as follows:

Orders are highest on Weekends, Fridays/Saturday evenings.

There are maximum orders from the month of July and January

Classic category contributes to maximum sales & total orders.

Large size pizza contributes to the maximum sales.

The Thai chicken Pizza contributes to the maximum revenue

The Brie Carre Pizza contributes to the minimum revenue

Based on the analysis, I recommend the following:

Recruitment of temporal staffs to take shifts during weekends

employing more contract workers during the month of July and January

More attention to Classic and Large pizza size as they bring in biggest revenue.