https://github.com/caesaredia/la-cafe-market-analysis

A data-driven feasibility study exploring the potential of launching a robot-staffed café in Los Angeles, based on real F&B business data.
https://github.com/caesaredia/la-cafe-market-analysis

business-intelligence cafe data-analysis data-visualization food-industry franchise los-angeles market-research pandas python

Last synced: 2 months ago
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A data-driven feasibility study exploring the potential of launching a robot-staffed café in Los Angeles, based on real F&B business data.

• Host: GitHub
• URL: https://github.com/caesaredia/la-cafe-market-analysis
• Owner: caesaredia
• Created: 2025-04-10T06:58:21.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-04-10T07:20:43.000Z (over 1 year ago)
• Last Synced: 2025-10-20T12:51:45.603Z (9 months ago)
• Topics: business-intelligence, cafe, data-analysis, data-visualization, food-industry, franchise, los-angeles, market-research, pandas, python
• Language: Jupyter Notebook
• Homepage:
• Size: 392 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
#  Café in Los Angeles — A Data-Driven Feasibility Study

Welcome to the future of coffee! This project explores whether launching a **robot-staffed café** in the heart of Los Angeles is a brilliant idea (spoiler alert: it just might be). Using real-world data and a healthy dose of Python-powered curiosity, this study digs into LA’s food and beverage landscape to see how our café concept stacks up.

## 📌 What’s This About?

In a city buzzing with food trends, we're thinking about opening a small café where the servers are… **robots**. Cool, right? But cool doesn't always mean viable. So before pitching this to investors, we asked the right questions:

- What types of food businesses dominate in LA?

- How big are they, really?

- Where are the busiest streets for dining?

- Are franchises still hot?

- How much seating is *too much* seating?

This project is a full analysis of Los Angeles’ dining business environment, especially through the lens of launching a tech-savvy, robot-run café.

## 🧠 Project Highlights

- **Exploratory Data Analysis**  

  Looked into types of food businesses, seating capacities, franchise proportions, and more.

- **Data Cleaning & Processing**  

  Nulls handled, snake_case engaged, street names parsed — the usual wrangling magic.

- **Location Intelligence**  

  Identified top streets for potential foot traffic and high-density dining zones.

- **Seating Distribution Analysis**  

  From compact cafés to giant restaurants — how many seats are enough?

## Author

Nabilla Hafsah Caesaredia