The Exploratory Data Analysis (EDA) of the Zomato Dataset project aims to uncover insights from Zomato's restaurant data through thorough data analysis and visualization techniques. This project helps understand trends, patterns, and relationships in the restaurant industry.

Objectives:- Analyze Restaurant Data: Explore various attributes of restaurants such as ratings, locations, cuisines, and pricing. Identify Trends: Detect trends and patterns in the restaurant industry. Visualize Insights: Create visualizations to convey key findings and insights effectively.

Data Sources:- The project utilizes the Zomato dataset, which includes: Restaurant names and locations Ratings and reviews Cuisine types Pricing information Operational details

Tools and Technologies:- The analysis is performed using the following tools and technologies:

Python: For data processing and analysis. Pandas: For data manipulation and analysis. Matplotlib & Seaborn: For creating visualizations and plots. Jupyter Notebook: For interactive analysis and reporting.

Key Features:- Data Cleaning: Handling missing values and correcting data types. Descriptive Statistics: Summarizing the main characteristics of the data. Visualization: Plotting various graphs and charts to reveal insights. Correlation Analysis: Identifying relationships between different attributes.

Project Structure:- The project is organized into the following directories:

data/: Contains the Zomato dataset. notebooks/: Jupyter notebooks for exploratory data analysis and visualizations. scripts/: Python scripts for data cleaning and preprocessing. reports/: Generated reports and visualizations. README.md: Project documentation and overview.