This is an end-to-end data analytics project on the Blinkit E-commerce dataset. The goal was to identify operational inefficiencies and key revenue drivers across different store types and product categories.
This project demonstrates my ability to execute the full analytics pipeline: SQL (Data Wrangling) → Python (Statistical Modeling) → Power BI (Business Reporting).
Conduct a comprehensive analysis of:
- Sales performance
- Customer satisfaction
- Inventory distribution
To generate actionable insights and visual reports using Power BI dashboards.
- The project is based on a comprehensive dataset This dataset provides a granular look at product and outlet performance across different geographical and operational segments. The core file, contains over 8,500 individual transaction records and features 12 key attributes, allowing for both macro-level KPI calculation and micro-level diagnostic analysis.
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Clone this repository
git clone https://github.com/AmeeJoshi-MCA/Blinkit-Analysis.git
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Open the file in Power BI Desktop.
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Explore dashboards and visualizations.
- Connect to your database (MySQL, PostgreSQL, etc.).
- Run the SQL scripts in the
sql/folder to extract raw data and all kpi queries.
- Connect to your python tool/softwaree (Jupiter Notebook, Visual studio, pyCharm , etc.).
- Run the python scripts in the
python/folder to extract all kpi queries and visuals.
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Open the file in Microsoft Excel.
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Explore dashboards and visualizations.
This project successfully transitioned raw transactional data through a complete analytical pipeline, from SQL-based cleaning and KPI calculation to advanced statistical modeling in Python. The resulting Power BI dashboard serves as an actionable BI tool, providing leadership with clear, data-backed recommendations to optimize merchandising, prioritize inventory, and target operational improvements in underperforming stores.The combination of technical rigor and business focus demonstrates full competency in the data analyst role.