🛒 E-Commerce Sales Dashboard (End-to-End Data Pipeline)

> [Click here to view the Interactive Dashboard on Tableau Public] https://public.tableau.com/views/E-CommerceSalesDashboard_17681539261150/Dashboard1?:language=en-US&:sid=&:redirect=auth&publish=yes&showOnboarding=true&:display_count=n&:origin=viz_share_link <

📌 Project Overview

This project analyzes a dataset of 500,000+ online retail transactions to identify customer purchasing patterns and high-value markets.

The goal was to build a full-stack data pipeline—starting from raw dirty data, moving through a SQL data warehouse, and ending with an interactive dashboard for stakeholders.

🛠️ Tech Stack

• Python (Pandas): Data cleaning, type conversion, and handling missing values.
• PostgreSQL: Relational database storage and aggregation (ETL).
• SQL: Writing views to calculate monthly revenue and top-selling products.
• Tableau: Building an interactive geospatial dashboard with filtering capabilities.

🔍 Key Insights

1. Seasonal Trends: Sales peak significantly in November/December, correlating with holiday shopping seasons.
2. Top Markets: While the UK is the primary market, significant growth opportunities exist in France and Germany.
3. Product Drivers: "Paper Craft" and decorative items are the highest volume drivers, suggesting a strong B2C customer base interested in gifts/crafts.

⚙️ Architecture (ETL Pipeline)

1. Extract: Raw CSV data loaded into Python.
2. Transform:
   • Removed cancelled transactions (negative quantities).
   • Parsed date strings into datetime objects.
   • Calculated total transaction value (Quantity * UnitPrice).
3. Load: Cleaned data pushed to a local PostgreSQL database.
4. Visualize: Connected Tableau to the data source to build the dashboard.

💻 How to Run Locally

1. Clone the Repo:

   git clone [https://github.com/hojundev/ecommerce-sales-dashboard.git](https://github.com/hojundev/ecommerce-sales-dashboard.git)

2. Install Dependencies:

   pip install pandas sqlalchemy psycopg2-binary python-dotenv

3. Database Setup:
   • Create a local PostgreSQL database named retail_db.
   • Create a .env file with your database password: DB_PASS=your_password.
4. Run the Pipeline:
   • Run notebooks/database_setup.ipynb to upload the data and create SQL views.