https://github.com/robson-python/online-sales---popular-market-data

https://github.com/robson-python/online-sales---popular-market-data

Last synced: 10 months ago
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• Host: GitHub
• URL: https://github.com/robson-python/online-sales---popular-market-data
• Owner: Robson-Python
• License: gpl-3.0
• Created: 2025-03-24T16:05:00.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-03-24T16:12:36.000Z (10 months ago)
• Last Synced: 2025-03-24T17:23:19.695Z (10 months ago)
• Language: Jupyter Notebook
• Size: 774 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Online-Sales - Popular-Market-Data

## **🛒Online Sales Data Analysis - Popular Market Data**

**Project**

Global transactions across multiple product categories

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**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:**

1. Analyze sales trends over time to identify seasonal patterns or growth opportunities.

2. Explore the popularity of different product categories across regions.

3. Investigate the impact of payment methods on sales volume or revenue.

4. Identify top-selling products in each category to optimize inventory and marketing strategies.

5. Evaluate the performance of specific products or categories in different regions to adapt marketing campaigns accordingly.

6. Were there any discounts given on purchases?

## **Data Source**

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](https://www.kaggle.com/datasets/shreyanshverma27/online-sales-dataset-popular-marketplace-data)

**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.**

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Software and equipment used for development: Python 3.12.9

Libraries (Pandas, Numpy, Matplotlib, Seaborn)

Operating system: Windows 11 Home Single Language.

Computer: HP 256 G9 - Intel Core i3.

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**📈 Análise de Dados**

**© 2025 - by Robson Silva - Programador Python e Analista de Dados.**

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