🛒 E-Commerce Sales Analysis

Status: Active Jupyter Notebook
Visit the project notebook for full analysis: [Notebook Link]
An analytical study of e-commerce transactions from an online retail dataset. The dataset contains thousands of invoices with product details, customer information, and sales records. The analysis provides insights into sales performance, customer behavior, and profitability across regions and product categories.

🌟 Overview

This project explores sales trends, customer purchasing behavior, and product performance in an e-commerce business.
The dataset includes:

• Transactional details (InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice)
• Customer information (CustomerID, Country)

The notebook walks through the process of:

• Data preprocessing and cleaning
• Exploratory Data Analysis (EDA)
• Sales and customer segmentation analysis
• Visualization of sales performance across countries, time, and categories
• Profitability insights
  The goal is to understand e-commerce sales dynamics and support data-driven decision making.

✨ Key Features

• Dataset of ~500K+ transaction records
• Analysis of sales performance based on:
  • Country/Region
  • Product categories and stock codes
  • Customer segmentation
  • Time-based trends (daily, monthly, yearly)
• Visualizations showing top products, sales trends, and customer distribution
• Business insights into revenue drivers and potential areas of growth

💻 Technology Stack

Technology	Description
Python	Core programming language for analysis
Pandas	Data manipulation and cleaning
NumPy	Numerical operations
Matplotlib & Seaborn	Data visualization
Scikit-learn	Optional machine learning tasks
Jupyter Notebook	Interactive analysis environment

📸 Sample Outputs

• Top 10 best-selling products
• Monthly and yearly sales trends
• Country-wise revenue contributions
• Customer segmentation insights

🚀 Getting Started

Prerequisites

Make sure you have Python 3.x installed.
Install the required libraries:
pip install pandas numpy matplotlib seaborn scikit-learn jupyter Clone the repository:

Copy code

git clone https://github.com/your-username/ecommerce-sales-analysis.git
cd ecommerce-sales-analysis

Start Jupyter Notebook: bash Copy code jupyter notebook Open the file E-Commerce Sales Analysis.ipynb and run all cells.

📁 Project Structure

python
Copy code
├── dataset.zip                         # Compressed dataset of sales transactions  
├── E-Commerce Sales Analysis.ipynb     # Jupyter Notebook with analysis  
├── README.md                           # Project documentation  
└── requirements.txt                    # Python dependencies (optional)

📂 Dataset You can download the dataset from here. (Ensure the file is placed in the same directory as the notebook before running analysis.)

⚠️ Disclaimer

This project is for educational and research purposes only. The dataset represents anonymized e-commerce sales transactions and should not be used for commercial decision-making without validation.

📄 License

This project is licensed under the MIT License - see the LICENSE.md file for details.