Meta-certified Data Analyst · Swiggy Builders Club · Python · SQL · Power BI · Open to internships

leetcode

github

Gaurangi Tyagi  • Peerlist

What problem does this solve? India's dark kitchen industry is growing fast — but founders and operators have no data-driven way to identify where demand exists but supply is thin. This tool answers the question: where should the next cloud kitchen open in Delhi NCR?What it does The Swiggy Dark Kitchen Opportunity Finder analyzes restaurant supply vs cuisine demand across 20 Delhi NCR pin codes. It computes a weighted gap score for every area-cuisine combination — comparing city-wide demand signals against local restaurant density — and classifies each opportunity as Gold, Silver, or Bronze tier.Key features<ul><li>Interactive Folium choropleth map of Delhi NCR coloured by opportunity score</li><li>Cuisine-level gap analysis across 20 pin codes (Connaught Place to Greater Noida West)</li><li>Streamlit dashboard with zone, cuisine, and tier filters</li><li>Scatter plot of supply vs demand to spot outlier opportunities</li><li>Built on Swiggy's official MCP Builders Club platform architecture</li></ul>Tech stack Python · PostgreSQL · Streamlit · Folium · Plotly · Pandas · Swiggy MCP APIStatus Live and deployed on HuggingFace Spaces. Production API access applied for via Swiggy Builders Club — currently running on sandbox data.