WHAT ACTUALLY DRIVES SALES INSIDE A BLINKIT DARK STORE?| I ANALYZED 8,500+ RECORDS TO FIND OUT.
Quick commerce is a ₹5 billion industry built on a simple promise your groceries in 10 minutes. But inside those dark stores, what actually determines whether one outlet earns ₹500 per product or ₹2,400? I spent weeks analyzing 8,523 Blinkit sales records using Python to find out. Some answers were expected. Others genuinely surprised me. THE PROJECT -WHAT I WAS TRYING TO ANSWER For my Analytics project , I picked a dataset that felt real Blinkit's product-outlet sales records. It had 8,523 rows and 12 variables: product type, weight, MRP, fat content, outlet size, location tier, outlet format, and more. The target variable was Item Outlet Sales, the revenue each product generated at each outlet. My goal was simple but important: figure out which factors actually drive sales performance. And then build a regression model to predict it. Quick numbers: - 8,523 sales records analyzed - 12 variables in the dataset - ₹1,499 average item outlet sales - 16 product...