🍽️ Zomato Market & Restaurant Analysis

📌 Project Overview

This project performs end-to-end data cleaning and exploratory analysis on large-scale restaurant data, encompassing over 123,000 restaurant records across 50+ cities.

The analysis focuses on understanding city-wise restaurant density, pricing behaviour, ratings, cuisine distribution, and online delivery trends, converting raw marketplace data into business-relevant insights.

Link to the Original Dataset -- https://www.kaggle.com/datasets/gauravkumar2525/zomato-restaurant-dataset

🎯 Objectives

• Analyse restaurant distribution across cities
• Study pricing patterns and affordability segments
• Understand the relationship between price and ratings
• Identify popular cuisines by market
• Evaluate online delivery penetration

---

🗂 Dataset Description

• Records: 123,000+
• Coverage: 50+ cities
• Granularity: Restaurant-level
• Key attributes:
  • City
  • Restaurant name
  • Cuisines
  • Average cost for two
  • Ratings
  • Votes
  • Online delivery availability

📌 The dataset enables market, pricing, and consumer behavior analysis at scale.

---

🛠 Tools & Technologies

• Python
  • Pandas
  • NumPy
• Visualization
  • Matplotlib
  • Seaborn
• Jupyter Notebook

---

🔍 Data Understanding & Exploration

• Dataset shape and structure analysis
• City-wise record distribution
• Rating and cost range inspection
• Identification of missing and inconsistent values

---

🔧 Data Cleaning & Preparation

Key steps performed:

• Removed duplicate restaurant entries
• Standardised city and cuisine names
• Handled missing ratings and cost values
• Converted pricing columns to numeric format
• Cleaned and split multi-cuisine fields
• Validated rating and vote ranges

---

🧠 Feature Engineering

• Price segmentation (budget / mid-range / premium)
• Aggregated restaurant counts by city
• Cuisine frequency analysis
• Rating and vote-based performance indicators
• Online delivery penetration metrics

---

📊 Exploratory Data Analysis

🔹 City-wise Market Analysis

• Restaurant density by city
• Competitive intensity across cities

🔹 Pricing & Affordability Analysis

• Average cost distribution
• Price bands by city
• Pricing vs customer ratings

🔹 Cuisine Analysis

• Most popular cuisines across cities
• Cuisine diversity by market

🔹 Ratings & Customer Feedback

• Rating distribution
• Vote count vs rating relationship

🔹 Online Delivery Trends

• Delivery availability by city
• Delivery penetration across price segments

---

📈 Key Insights

• Restaurant density is highly concentrated in a few major cities
• Higher pricing does not always correlate with higher ratings
• Certain cuisines dominate across most cities
• Online delivery adoption varies significantly by market and price segment

---

📁 Project Structure

📦 Zomato-analysis ┣ 📂 data ┃ ┗ Zomato-datasets.zip ┣ 📂 notebooks ┃ ┗ Zomato.ipynb ┃ ┗ Zomato_Visuals.ipynb ┣ 📄 README.md ┣ 📄 requirements.txt ┗ 📄 .gitignore