Purpose: Analyze sales data to identify trends, top-selling products, and revenue metrics for business decision-making.

Description: In this project, you will dive into a large sales dataset to extract valuable insights. You will explore sales trends over time, identify the best-selling products, calculate revenue metrics such as total sales and profit margins, and create visualizations to present your findings effectively. This project showcases your ability to manipulate and derive insights from large datasets, enabling you to make data-driven recommendations for optimizing sales strategies.

Additional Tools: Excel or data visualization tools like Tableau or Power BI can help you create visually appealing charts and graphs to present your findings effectively.

• Excel: Used for data manipulation, calculations, and basic visualizations.
• Data visualization tools like Tableau, Power BI, or Google Data Studio: Used for creating interactive and visually appealing dashboards and charts.

Sales Analysis

About the Dataset

The dataset consists of 11 columns, each column representing an attribute of purchase on a product –
Order ID – A unique ID for each order placed on a product
Product – Item that is purchased
Quantity Ordered – Describes how many of that products are ordered
Price Each – Price of a unit of that product
Order Date – Date on which the order is placed
Purchase Address – Address to where the order is shipped
Month, Sales, City, Hour – Extra attributes formed from the above.

Acknowledgements

Dataset is downloaded and compiled from KeithGalli’s GitHub repository on Pandas Data Science Tasks.
You find and access the repository here – https://github.com/KeithGalli/Pandas-Data-Science-Tasks