This project aims to provide actionable insights that can drive strategic business decisions and enhance an online retail company's overall performance in the competitive retail market. In this project, I am working with transactional data from an online retail store. The dataset contains information about customer purchases, including product details, quantities, prices, and timestamps.
Using exploratory data analysis, my goal is to provide insights into the store's sales trends such as the busiest sales months, customer behavior such as the store's most valuable customers, and popular products.
The dataset contains transactional data of an online retail store from 2010 to 2011. The dataset is available as a .xlsx file and can also be downloaded here. You can also find more information at UC Irvine's Machine Learning website.
The dataset contains the following columns:
- InvoiceNo: Invoice number of the transaction
- StockCode: Unique code of the product
- Description: Description of the product
- Quantity: Quantity of the product in the transaction
- InvoiceDate: Date and time of the transaction
- UnitPrice: Unit price of the product
- CustomerID: Unique identifier of the customer
- Country: Country where the transaction occurred