🛒 Data Analytics Python Project - Blinkit

This project dives into a dataset of products listed on Blinkit — one of India’s leading instant grocery delivery platforms. Using Python and powerful data visualization tools, I explored key trends, pricing strategies, and product distribution patterns across categories and cities.

🎯 Objective

To conduct a comprehensive analysis of Blinkit's business operations and customer behavior using Python — focusing on sales trends, product attributes, outlet performance, and location-based insights.

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✅ KPI Metrics

• Total Sales: Overall revenue generated from all items sold.
• Average Sales: Average revenue per sale.
• Number of Items: Total count of different items sold.
• Average Rating: Average customer rating for sold items.

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📊 Visualization & Chart Requirements

1. Total Sales by Fat Content

• Objective: Analyze the impact of fat content on total sales.
• Chart Type: Pie Chart

2. Total Sales by Item Type

• Objective: Evaluate performance across different item types.
• Chart Type: Column Chart

3. Fat Content by Outlet for Total Sales

• Objective: Compare total sales across outlets segmented by fat content.
• Chart Type: Double Column Chart

4. Total Sales by Outlet Establishment

• Objective: Understand how the age/type of outlet establishment affects total sales.
• Chart Type: Line Chart

5. Sales by Outlet Size

• Objective: Analyze how outlet size correlates with total sales.
• Chart Type: Pie Chart

6. Sales by Outlet Location

• Objective: Assess geographic distribution of sales across different regions.
• Chart Type: Bar Plot

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🛠️ Tools & Technologies

• Python
• Pandas
• Matplotlib
• Seaborn

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