This is a project where I attempted to clean and transform a dataset using Power BI, implementing appropriate measures and visualize the findings.
In this project, I assumed the role of an analyst at a company that sells scale model cars. The goal of this project is to model and analyze data from their sales records database using Power BI directly in the browser to extract insights that drive business decision-making. By applying data cleaning, transformation, and modeling techniques, the sales data will be ready to analysis and develop practical skills in working with real-world datasets.
I attempted to create a report to answer the following three questions:
1. What were the total sales for the company?
2. Which market generated the most sales on average?
3. What were the profits by segment? Which segment has the most profit?
The dataset (see the csv file in the repository) contains data on order details of customers for orders of a superstore in the U.S. Here are the columns and the data it contains:
Order ID: all order IDs
Customer Name: customer name
Segment: segments of product
Country: country of sales
Market: continent/market of sales
Product ID: list of product IDs
Category: category of different product
Sub-Category: sub-category of product
Product Name: product name
Sales: sales of different product
Quantity: quantity of product sale
Profit: profit made
To successfully obtain the outcomes of this project, I have perform the following tasks:
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Import the sales data into Power BI
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Clean and transform the data for analysis
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Design a basic data model in Power BI
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Create calculated columns and measures using DAX
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Build visualizations to extract insights from the data
By the end of this project, I have found out that:
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The total sales for the company is $12.64M.
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By average Lesotho has the highest sales.
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In 2016, the profits gained by all segments is $220,890, with the conusumber sector gained the most profit over all other segments.