An end-to-end Blinkit Analysis Project integrating Python(EDA & data cleaning) , PostgreSQL (business queries),and Power BI (interactive dashboard).Demonstrated skills in ETL , SQL , data visualization , building business insights , data analysis.
Project Scope & Responsibilities: Performed data cleaning and preprocessing using Python (Pandas, NumPy), including handling missing values, standardizing categories, and feature creation.
Designed and executed SQL queries in PostgreSQL to analyze sales by outlet type, location tier, item category, fat content, and customer ratings.
Built a dynamic Power BI dashboard with KPIs such as Total Sales, Average Sales, Average Rating, and Number of Items.
Conducted exploratory data analysis (EDA) to identify trends in outlet establishment growth, product performance, and customer buying behavior.
Implemented interactive slicers and filters to enable drill-down analysis by outlet size, location, and item type.
📊 Key Business Insights
Tier 3 outlets generate the highest total sales, indicating strong demand in emerging locations.
Fruits & Vegetables and Snack Foods are the top-performing categories.
Low-fat products contribute significantly to total revenue, reflecting changing customer preferences.
Medium-sized outlets show an optimal balance between sales volume and customer ratings.
Outlet establishment trends highlight consistent business growth over time.
🛠 Tools & Technologies
Python | Pandas | NumPy | SQL | PostgreSQL | Power BI | DAX | Data Cleaning | EDA | Data Visualization