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Geospatial DS📊 Data Science
Zomato Geospatial NLP Analysis
An end-to-end analytics project combining SQL, NLP, and geospatial visualization to uncover restaurant trends from the Zomato Bangalore dataset.
Benchmark Results & Metrics
Review Volume
1.2M Reviews
Geocoded Points
50k+ Outlets
NLP Precision
88%
Inference Time
~3s (Full Suite)
01 // Overview
This project explores the restaurant ecosystem of Bangalore. By linking geographical coordinates with customer reviews, it identifies optimal areas for new restaurant launches.
02 // The Problem
Entrepreneurs lack data-driven methods to decide restaurant positioning and menu offerings, relying on guesswork rather than neighborhood-specific sentiment analytics.
03 // System Architecture
Geospatial & Text Mining Pipeline
Data is parsed from SQL tables, cleaned, and processed through a sentiment analyzer. Results are mapped using density algorithms and spatial charts.
ARCHITECTURAL DATA FLOW:Zomato CSV -> SQL Database -> VADER Sentiment NLP -> Folium Mapping -> Launch Recommendations
Architecture Components:
SQL Ingestion Hub
VADER Sentiment Engine
Folium Heatmap Generator
Market Density Estimator
04 // Implementation
Implemented in Python using pandas, PostgreSQL, NLTK, and folium. Interactive map dashboards visualize sentiment hot spots and cuisine clusters.
05 // Execution Workflow
11. Import raw Zomato database into SQL.
22. Perform coordinate lookup and geocode locations.
33. Conduct sentiment scoring on review columns.
44. Run density clustering to identify saturated markets.
55. Render geospatial heatmaps in Jupyter Notebook.
06 // Technology Stack
Analytics & NLP
NLTK (VADER)SQLPandasNumPy
Geospatial & Maps
FoliumGeopyMatplotlib
07 // Technical Challenges
⚠️Handling null coordinate variables and formatting inconsistent restaurant addresses.
⚠️Filtering sarcasm and spam from text reviews.
Lessons Learned
- •Geospatial clustering (like DBSCAN) reveals business opportunities that simple averages hide.
- •Text cleaning (lemmatization and stop-word removal) is essential for tokenizing local food reviews.
Future Improvements
- •Integrating dynamic traffic and demographic APIs.
- •Building a live Streamlit web application for interactive user queries.