🍽️ Zomato Dataset Exploratory Data Analysis (EDA)

This project performs a comprehensive exploratory data analysis (EDA) on the Zomato dataset. The goal is to uncover patterns and insights related to restaurant distributions, user ratings, online delivery services, and geographical variations.

📊 Table of Contents

• Project Overview
• Analysis Pipeline
• Key Insights & Visualizations
• Tools Used
• How to Run
• Future Work

Project Overview

This analysis uses two datasets:

• zomato.csv: Contains detailed information about restaurants, including location, cuisines, cost, and ratings.
• Country-Code.xlsx: A mapping file to link country codes with their respective country names.

The primary objective is to clean, merge, and analyze this data to answer key business questions, such as:

• What is the geographical spread of Zomato's listings?
• What do user ratings tell us about customer satisfaction?
• Which countries and cities have the highest concentration of restaurants?
• Where is the online delivery service most prevalent?

🚀 Analysis Pipeline

1. Data Loading: Imported the zomato.csv and Country-Code.xlsx files using Pandas.
2. Data Cleaning & Preprocessing:
   • Merged the two datasets on "Country Code" to add full country names to the main dataframe.
   • Checked for missing values. Found and visualized 9 missing entries in the 'Cuisines' column using a Seaborn heatmap.
3. Exploratory Data Analysis (EDA):
   • Analyzed the geographical distribution of restaurants by country and city.
   • Investigated the relationship between Aggregate rating, Rating color, and Rating text.
   • Examined the distribution and origin of zero ratings.
   • Identified which countries offer online delivery.
   • Mapped currencies to their respective countries.

📈 Key Insights & Visualizations

🌎 Geographical Distribution

• Top 3 Countries: The vast majority of Zomato's listings are in India (94.39%), followed by the United States (4.73%) and the United Kingdom (0.87%).

• Top 5 Cities: The top 5 cities with the most restaurant listings are all within the National Capital Region (NCR) of India:

  1. New Delhi (68.87%)
  2. Gurgaon (14.07%)
  3. Noida (13.59%)
  4. Faridabad (3.16%)
  5. Ghaziabad (0.31%)

⭐ Rating Analysis

• Rating Distribution: A significant number of restaurants (2,148) have a rating of 0.0 ("Not Rated"). This suggests many new listings or a low rate of user feedback for certain entries.
• Common Ratings: The most frequent actual ratings fall in the "Average" category (Orange color), specifically between 2.5 and 3.4.
• Zero Ratings: The overwhelming majority of "Not Rated" (0.0) entries originate from India (2,139 out of 2,148).

🚀 Service Analysis

• Online Delivery: The online delivery service is primarily available in India and the UAE. Other countries in this dataset do not appear to have this feature enabled.
• Currency: The analysis successfully maps various currencies (like Botswana Pula, Brazilian Real, Dollar, Pounds, and Indian Rupees) to their corresponding countries.

💻 Tools Used

• Python
• Pandas: For data manipulation and analysis.
• NumPy: For numerical operations.
• Matplotlib: For basic data visualization.
• Seaborn: For advanced statistical visualization.
• Jupyter Notebook: As the development environment.

🛠️ How to Run

1. Clone the repository:

   git clone https://github.com/your-username/your-repository-name.git

2. Install the required libraries:

   pip install pandas numpy matplotlib seaborn jupyterlab openpyxl

3. Launch Jupyter Notebook or Jupyter Lab:

   jupyter lab

4. Open the .ipynb file and run the cells sequentially.

🔮 Future Work

The notebook identifies a next step which can be implemented:

• Analyze the Cuisines column to find and visualize the Top 10 most popular cuisines across the dataset.