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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).**\n\nAfter 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.\n\nThis project demonstrates skills in **data engineering, data analysis, business intelligence (BI),** and visual storytelling by transforming raw transactional data into clear, data-driven decisions.\n\n# Key Highlights \u0026 Keywords\n**End-to-end pipeline:** SQL → Python → Excel → Power BI\n\n**Data Cleaning \u0026 EDA:** Pandas, Matplotlib, Seaborn\n\n**BI \u0026 Reporting:** Interactive Power BI dashboards, DAX measures, drill-through analysis\n\n**KPI development:** Revenue trends, customer behavior, seasonal analysis, product performance\n\n**Business Impact:** Enables data-driven decisions by analyzing Shopping Trends across multiple dimensions\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnour-zayed%2Fshopping-trends-analytics-sql-python-power-bi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnour-zayed%2Fshopping-trends-analytics-sql-python-power-bi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnour-zayed%2Fshopping-trends-analytics-sql-python-power-bi/lists"}