Zomato Dataset: Exploratory Data Analysis (EDA)

This project involves performing Exploratory Data Analysis (EDA) on the Zomato dataset to uncover insights related to restaurant ratings, locations, and cuisine trends. As a beginner, this project helped me understand the data and prepare it for further analysis or predictive modeling.

Project Overview

The Zomato dataset provides information about restaurants, including their locations, cuisines, and customer ratings. The main goals of this project are:

• Explore the dataset to identify key patterns.
• Visualize important features like restaurant ratings, popular cuisines, and city-wise distributions.
• Extract meaningful insights for better decision-making.

Key Steps in the Project

1. Data Preprocessing:

   • Cleaned and filtered the dataset to remove irrelevant or missing values.
   • Extracted specific columns like Restaurant Name, City, Cuisines, and Rating.

2. Exploratory Data Analysis (EDA):

   • Visualized data using bar charts, pie charts, and histograms to show the distribution of restaurants across cities and their corresponding ratings.
   • Analyzed the most popular cuisines across different cities.
   • Investigated which cities had the highest number of top-rated restaurants.

3. Feature Extraction:

   • Focused on identifying cuisines that contribute to high ratings.
   • Created visual representations of data to find meaningful patterns.

Next Steps

I plan to use this analysis as a foundation for building predictive models or making data-driven recommendations for improving restaurant performance.

Libraries Used

• pandas
• numpy
• matplotlib
• seaborn

How to Run the Project

1. Clone the repository:

   git clone https://github.com/YashsTiwari/EDA-Zomato-dataset.git

2. Install the required libraries:

   pip install -r requirements.txt

3. Open and run the Jupyter notebook to view the EDA.