Online Retail Data Analysis Project (Python + SQL + Power BI)

This repository contains a complete end-to-end data analysis project on the Online Retail II dataset, incorporating Python for data cleaning, SQL for querying and analytics, and Power BI for interactive dashboard creation. The goal is to extract actionable business insights and build a clear understanding of customer behavior.

Project Structure

. ├── data_cleaning/ # Jupyter notebooks/scripts for cleaning raw dataset ├── sql_scripts/ # SQL queries for analytical tasks ├── PowerBI_dashboard # [To be added] Final dashboard file ├── README.md # Project overview and documentation

Dataset Information

• Source: UCI Machine Learning Repository: Online Retail II Dataset
• Alternative (Cleaned): Kaggle Cleaned Dataset
• Content: Transactions made by a UK-based online retailer over 2 years (2009–2011), including:
  • InvoiceNo, StockCode, Description, Quantity
  • InvoiceDate, UnitPrice, CustomerID, Country

Tools & Technologies Used

• Python (Pandas, NumPy) – Data cleaning and preprocessing
• SQL (PostgreSQL) – Data querying and aggregation
• Power BI – Dashboard creation and business intelligence

Key Analyses Performed

• RFM Segmentation (Recency, Frequency, Monetary)
• Cohort Analysis (Customer retention over time)
• Sales KPIs (Revenue, Quantity, Orders)
• Time-Series Trends (Monthly/Weekly sales)
• Customer Distribution by Country
• Top Products and Revenue Drivers

How to Use

1. Clone the repository:

   git clone https://github.com/Md-ShahNawaj/online-retail-data-analysis-python-sql-powerbi.git

2. Review Python notebooks in the data_cleaning/ folder.

3. Run the SQL queries from the sql_scripts/ folder on your PostgreSQL/MySQL environment after importing the cleaned dataset.

4. Open the Power BI dashboard (when added) to explore interactive visualizations.

Sample Visuals (Power BI) – Coming Soon

Stay tuned! Power BI report screenshots and .pbix file will be uploaded soon.