Zomato Data Analysis Project

Overview

This project involves analyzing Zomato data to understand various aspects of restaurant performance and customer preferences. The analysis aims to answer key business questions about restaurant types, customer ratings, and order behaviors using Python and its libraries.

Technologies Used

• Python: Programming language used for data analysis.
• Pandas: Library for data manipulation and analysis.
• NumPy: Library for numerical operations.
• Matplotlib.pyplot: Library for creating static, animated, and interactive visualizations.
• Seaborn: Library for statistical data visualization.

Objectives

1. Understand the Business Problems:
   1. What type of restaurant do the majority of customers order from?
   2. How many votes has each type of restaurant received from customers?
   3. What are the ratings that the majority of restaurants have received?
   4. Zomato has observed that most couples order most of their food online. What is their average spending on each order?
   5. Which mode (online or offline) has received the maximum rating?
   6. Which type of restaurant received more offline orders, so Zomato can provide those customers with some good offers?

Conclusion

This analysis provides insights into customer preferences and restaurant performance on Zomato. The visualizations and findings can help Zomato make informed decisions to improve customer experience and tailor their offerings.

Setup and Installation

1. Clone the repository:

   git clone https://github.com/yourusername/zomato-data-analysis.git

2. Navigate to the project directory:

      cd zomato-data-analysis

3. Install the required libraries::

   pip install pandas numpy matplotlib seaborn

4. Place your dataset (Zomato_data.csv) in the project directory.

5. Run the analysis script: