🍴 Zomato Data Analysis Using Python

📖 Overview

This repository contains a company-standard implementation of Exploratory Data Analysis (EDA) on Zomato’s restaurant dataset.
The analysis highlights key insights into customer preferences, restaurant trends, and business opportunities by examining:

• Online vs offline ordering behavior
• Popular restaurant categories
• Cost preferences for couples
• Rating distributions and voting trends

The project uses Python and industry-standard libraries: Pandas, NumPy, Matplotlib, and Seaborn.

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📂 Repository Structure

├── data/                   # Dataset files (CSV)
├── notebooks/              # Jupyter/Colab notebooks
├── docs/                   # Documentation & visualizations
├── LICENSE                 # License information
├── README.md               # Project documentation

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⚙️ Prerequisites

• Python 3.8+
• Jupyter Notebook or Google Colab
• Recommended: Virtual environment for dependencies

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🚀 Getting Started

1. Clone the repository

zomato-data-analysis-using-python/notebooks/pythoncode/

Here’s the full clone and navigation code:

# Clone the repository
git clone https://github.com/Rasheeda-Sultana/zomato-data-analysis-using-python.git

# Move into the project folder
cd zomato-data-analysis-using-python

# Navigate to the python script inside notebooks
cd notebook

2. Prepare the dataset

Place the dataset file Zomato-data-.csv inside the data/ folder.

3. Run the notebook

jupyter notebook notebooks/zomato_python_analysis.ipynb

4. Execute the workflow

Run all notebook cells sequentially to perform:

• Data cleaning
• Visual exploration
• Insights generation

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📊 Key Analysis Performed

• Data Cleaning & Preparation – handled missing values and standardized ratings
• Restaurant Type Exploration – analyzed common dining categories
• Votes by Category – measured customer preferences across restaurant types
• Most Voted Restaurant – identified the top restaurant by popularity
• Online vs Offline Orders – assessed online order acceptance
• Rating Distribution – histogram analysis of customer ratings
• Couple Cost Preferences – identified price ranges for two-person dining
• Online vs Offline Ratings – compared customer satisfaction
• Order Mode vs Type Heatmap – mapped relationship between order mode and restaurant type

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🖼️ Sample Visualizations

Restaurant Types Distribution

Online vs Offline Orders

Heatmap of Order Mode vs Restaurant Type

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🛠️ Tech Stack

• Python 3.8+
• Pandas & NumPy – Data processing and analysis
• Matplotlib & Seaborn – Visualization and reporting
• Jupyter Notebook / Colab – Interactive development environment

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📌 Future Enhancements

• Automate ingestion of live datasets from APIs
• Apply advanced statistical and machine learning models
• Deploy interactive dashboards via Power BI / Streamlit

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🏷️ Tags

#Python #DataAnalysis #EDA #Zomato #Pandas #Visualization #Seaborn #Matplotlib

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🛡️ License

This project is licensed under the MIT License. You are free to use, modify, and share this project with proper attribution.

🌟 About Me

Hi there! I'm Rasheeda Sultana. I’m a fresher in Data Analytics and passionate learner on a mission to share knowledge to help others learn and make working with data enjoyable and engaging! 📧 Contact: rasheeda7751@gmail.com