Zomato Web Scraping Project

Project Overview

This project automates the process of scraping data from Zomato's website to collect valuable information on restaurants. The data includes essential metrics like restaurant names, cuisines, prices, and ratings, which are crucial for analyzing food trends and customer preferences in the industry.

Features

• Automated Web Scraping: Uses Selenium to navigate through Zomato's pages and scrape restaurant data.
• Data Cleaning and Transformation: Handles encoding issues and cleans data for easier analysis and usability.
• Data Storage: Saves the scraped data to a CSV file, making it accessible for further analysis.
• Analysis-Ready Data: Ensures that data is structured and prepared for visualization and analysis.

Technologies Used

• Python: The primary language used for scripting and data processing.
• Selenium: Enables browser automation and web scraping from Zomato's site.
• Pandas: Facilitates data manipulation and cleaning.
• CSV: Chosen format for data storage, enabling easy import for analytical tools.

Prerequisites

Ensure the following are installed on your system before running the project:

• Python 3.x
• Selenium
• ChromeDriver (or another browser driver if preferred)
• Pandas

Running the Scraper

1. Open Zomato.ipynb in a Jupyter Notebook environment.
2. Run each cell sequentially to start the scraper and collect data.
3. The data will be saved as zomato_data.csv in the project directory.

Challenges and Solutions

This project addresses several key challenges:

• Dynamic Content Loading: Zomato's listings load dynamically, so the scraper scrolls through the page to capture all restaurant data.
• Encoding and Formatting: Handles special characters in names and prices to ensure data remains clean and readable.

Use Cases

With the gathered data, several analyses can be conducted:

• Restaurant Trend Analysis: Identify popular cuisines, restaurants, and pricing trends.
• Customer Sentiment Analysis: Use user reviews to gauge customer sentiment.
• Business Intelligence: Derive insights into customer preferences and pricing for informed decision-making.

Future Work

• Data Analysis and Visualization: Perform in-depth analyses to uncover insights.
• Sentiment Analysis: Analyze customer reviews for sentiment trends.
• Automation: Set up automated data collection for real-time updates.

Contributing

Contributions are welcome! If you have suggestions or would like to contribute, please feel free to open an issue or submit a pull request.