Project
Global transactions across multiple product categories
About the dataset
This dataset provides a comprehensive overview of online sales transactions across different product categories. Each row represents a single transaction with detailed information such as order ID, date, category, product name, quantity sold, unit price, total price, region, and payment method.
Columns:
Order ID: Unique identifier for each sales order.
Date: Date of the sales transaction.
Category: Broad category of the product sold (e.g., electronics, appliances, clothing, books, beauty products, sports).
Product Name: Specific name or model of the product sold.
Quantity: Number of units of the product sold in the transaction.
Unit Price: Price of one unit of the product.
Total Price: Total revenue generated by the sales transaction (Quantity * Unit Price).
Region: Geographic region where the transaction occurred (e.g. North America, Europe, Asia).
Payment method: Method used for payment (e.g. credit card, PayPal, debit card).
Insights:
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Analyze sales trends over time to identify seasonal patterns or growth opportunities.
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Explore the popularity of different product categories across regions.
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Investigate the impact of payment methods on sales volume or revenue.
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Identify top-selling products in each category to optimize inventory and marketing strategies.
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Evaluate the performance of specific products or categories in different regions to adapt marketing campaigns accordingly.
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Were there any discounts given on purchases?
This dataset was made available by ShreyanshVerma27 on Kaggle and is licensed under CC0: Public Domain:
hreyanshVerma27, Online Sales Dataset - Popular Marketplace Data. Available at: Kaggle Dataset Link
The purpose of this project is to train and put my skills into practice. I will also include it in my portfolio so I can showcase my skills and win future projects and clients in the area of Data Analysis and Science with Python.
Software and equipment used for development: Python 3.12.9
Libraries (Pandas, Numpy, Matplotlib, Seaborn)
Operating system: Windows 11 Home Single Language.
📈 Análise de Dados
© 2025 - by Robson Silva - Programador Python e Analista de Dados.