Introduction
The dataset under review is a retail sales data sample, containing information on sales transactions, including variables such as product codes, customer information, order quantities, sales, and dates. As a data analysis intern, I'm always on the lookout for fresh insights and trends, and this dataset is packed with goodies. In this "First Glance" report, I'll be sharing my initial observations and findings from a quick spin through the data.let's get started!
Observations
I did a quick review of this dataset using power query editor in power BI and Power BI desktop for the quick summary using line visuals.
This dataset contained 2823 rows and 25 columns, which are grouped into whole numbers datatypes, decimal datatypes, and text datatypes.
The columns are:
Order number, quantity ordered, price each, orderline number, Sales, order dates, status, QTR_ID, Month_ID, Year_ID, product line, MSRP, product code, customer name, phone, addressline1, addressline 2, State, postal code, country, territory, contact last name, contactfirst name, dealsize.
Anomalies
Three columns contains rows with empty cells. AddressLine 2 contained 89% empty rows, State column contained 51% empty rows and postalcode postal code contained 4% empty rows.
Trends/Insights
Sales distribution by country shows the USA is making more revenue compared to other countries.
The highest revenue was generated in November. It was a flowing trend until November, when it attained its peak.
The sales trend by productLine shows classic cars as the highest revenue-generating product.
Conclusion
This review of the retail dataset reveals insights into top-selling products, country sales trends, and monthly sales trends. Further analysis could explore the top 10 and bottom 10 selling products, regional market analysis, and customer segmentation. To learn more about data analysis and internship opportunities, visit HNG Internships {https://hng.tech/internship} or Check out HNG Premium{https://hng.tech/premium}
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