This project analyzes Blinkit sales data to uncover business insights such as top-selling products, seasonal trends, and revenue drivers.
It was built as an end-to-end data analytics project using SQL, Python, Excel, and Power BI.
To analyze Blinkit’s sales and customer data, clean and transform it, and build interactive dashboards that help in:
- Identifying top-selling products and categories
- Understanding seasonal sales trends
- Analyzing revenue contribution by products
- Supporting decision-making for business growth
- SQL → Data extraction, cleaning, and transformations
- Python → Exploratory Data Analysis (EDA) and visualization
- Excel → Pivot tables & static dashboard
- Power BI → Interactive business dashboard
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Data Cleaning & Preparation
- Removed duplicates, handled missing values
- Standardized categories and product names
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SQL Analysis
- Performed aggregations (SUM, COUNT, AVG)
- Used
GROUP BY,ORDER BY, and window functions for trends - Generated KPIs like total sales, top products, and yearly growth
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Python EDA
- Jupyter Notebook for data exploration
- Visualizations: sales trends, product distribution, top item
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Excel Dashboard
- Pivot tables for sales summary
- Charts showing product performance and sales by month
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Power BI Dashboard
- Interactive dashboard with filters
- KPIs: Revenue, orders, items
- Drilldowns for category- and product-level analysis
- 📈 Top 20% of products drive 70% of revenue (Pareto Principle)
- 🕒 Seasonal trends: Sales peak during festive months
- 💰 Beverages & Snacks are the highest revenue-generating categories
- 👥 Repeat customers contribute significantly to overall revenue
- Clone the repository:
git clone https://github.com/your-username/Blinkit-Analysis-Capstone-2025.git