https://github.com/nour-zayed/shopping-trends-analytics-sql-python-power-bi
"End-to-end Shopping Trends analytics project using SQL, Python, Excel & Power BI — data cleaning, EDA, KPI generation, and interactive dashboards with DAX for actionable business insights."
https://github.com/nour-zayed/shopping-trends-analytics-sql-python-power-bi
business-intelligence data-analysis data-visualization dax powerbi python sql
Last synced: about 1 month ago
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"End-to-end Shopping Trends analytics project using SQL, Python, Excel & Power BI — data cleaning, EDA, KPI generation, and interactive dashboards with DAX for actionable business insights."
- Host: GitHub
- URL: https://github.com/nour-zayed/shopping-trends-analytics-sql-python-power-bi
- Owner: Nour-Zayed
- Created: 2025-07-29T21:08:58.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-07-29T21:28:48.000Z (11 months ago)
- Last Synced: 2025-08-21T20:19:12.456Z (10 months ago)
- Topics: business-intelligence, data-analysis, data-visualization, dax, powerbi, python, sql
- Language: Jupyter Notebook
- Homepage:
- Size: 2.97 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Shopping-Trends-Analytics-SQL-Python-Power-BI
Shopping Trends Analytics Project is an end-to-end data analytics pipeline built using **SQL, Python, Excel, and Power BI.**
We started by importing the Shopping Trends dataset into a SQL Server database to ensure secure, structured storage and efficient querying. The database was then connected to Visual Studio Code, where we performed advanced data cleaning, exploratory data analysis (EDA), and statistical insights using**Python (Pandas, NumPy, Matplotlib, Seaborn).**
After processing and generating meaningful KPIs, the cleaned dataset was exported into an Excel sheet, serving as the single source of truth. Finally, we built a fully interactive**Power BI** dashboard with DAX measures and visualizations to uncover hidden shopping patterns and provide actionable business insights.
This project demonstrates skills in **data engineering, data analysis, business intelligence (BI),** and visual storytelling by transforming raw transactional data into clear, data-driven decisions.
# Key Highlights & Keywords
**End-to-end pipeline:** SQL → Python → Excel → Power BI
**Data Cleaning & EDA:** Pandas, Matplotlib, Seaborn
**BI & Reporting:** Interactive Power BI dashboards, DAX measures, drill-through analysis
**KPI development:** Revenue trends, customer behavior, seasonal analysis, product performance
**Business Impact:** Enables data-driven decisions by analyzing Shopping Trends across multiple dimensions