🛒 E-commerce Sales Analysis with SQL, Pandas, Matplotlib, Seaborn 🚀

Welcome to the E-commerce Sales Analysis repository! This project combines SQL, Pandas, Matplotlib, and Seaborn to analyze sales data and uncover insights for an online store. Explore transactional data, clean and transform it, and create visualizations to understand trends and performance metrics.

Feel free to Star ⭐ this repository and share it with others passionate about data analytics!

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🏆 Project Overview

This project focuses on analyzing e-commerce sales data to extract actionable insights. The analysis includes revenue trends, top-performing products, customer segmentation, and regional performance. The goal is to create a comprehensive sales analysis dashboard using Python and SQL.

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🗂️ Table of Contents

1. Introduction
2. Key Features
3. Installation Guide
4. How to Use
5. Project Structure
6. Contributing
7. License

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🔰 Introduction

This project aims to provide a complete analysis pipeline for e-commerce data, leveraging the following:

• SQL for structured querying of datasets.
• Pandas for data cleaning and manipulation.
• Matplotlib and Seaborn for visual storytelling.
• GitHub for version control and collaboration.

Whether you're a data enthusiast, analyst, or professional, this project is an excellent showcase of end-to-end data analysis.

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🚀 Key Features

• SQL Integration: Perform complex queries on an SQLite database.
• Data Cleaning: Handle missing values, duplicates, and inconsistencies using Pandas.
• Visualization: Gain insights through clear and interactive plots.
• Feature Engineering: Generate new metrics like customer lifetime value and revenue per product.
• Collaboration: Utilize GitHub for documentation, sharing, and version control.

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🛠️ Installation Guide

Step 1: Clone the Repository

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

Step 2: Install Required Libraries

Create a virtual environment (optional) and install dependencies:

pip install -r requirements.txt

Step 3: Set Up the SQLite Database

Download the dataset from the repository or provide your own data. Place it in the Data folder.

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🚦 How to Use

1. Run Colab/Jupyter Notebooks:

   • Use Google Colab or Jupyter to execute the analysis scripts.
   • Follow the instructions in the notebooks for each step of the project.

2. Customize the Queries:

   • Modify SQL queries in the SQL folder to suit your analysis needs.

3. Push Updates to GitHub:

   • Save progress, commit changes, and push to the GitHub repository for version control.

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🏗️ Project Structure

📂 ecommerce-sales-analysis/
├── 📄 README.md                              # Project overview and guide
├── 📂 Data/                               # Dataset files
│   └── Customers_Large.csv                    # customers data
│   ├── Orders_Large.csv                       # orders data
│   ├── Products_Large.csv                     # products data
│   ├── Sales_Large.csv                        # sales data
├── 📂 database/                           # Database files
│   └── database.db                            # SQLite database
├── 📂 SQL/                                # SQL scripts
│   ├── monthly_revenue.csv                     # Monthly revenue analysis
│   ├── revenue_by_regions.csv                  # Regional revenue analysis
│   ├── top_selling_products.csv                # Top-performing products analysis
│   └── revenue_by_segments.csv                 # Customer segmentation analysis
├── E_commerce_Sales_Analysis_with_SQL,_Pandas,_Matplotlib,_Seaborn.ipynb  # Jupyter notebooks
├── 📦 requirements.txt                        # List of required Python libraries
└── 📜 LICENSE                                 # License for the repository

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🤝 Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/your-feature

3. Commit your changes:

   git commit -m "Add your feature"

4. Push to your branch:

   git push origin feature/your-feature

5. Open a pull request, and we’ll review your changes!

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📜 License

This repository is licensed under the MIT License. See the LICENSE file for details.

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