Project Overview: This project is an Exploratory Data Analysis (EDA) on the Zomato dataset, aimed at extracting insights about restaurants, ratings, cuisines, and customer preferences. The analysis is performed using Python, leveraging libraries such as Pandas, NumPy, Matplotlib, and Seaborn.
Dataset: The Zomato dataset contains information on restaurants across various countries, including details such as:
Restaurant name Location Average cost for two Ratings Cuisines Online delivery and table booking options Number of votes And more. Objective
The objective of this project is to:
Understand the overall distribution and trends in restaurant ratings, cost, and services. Discover the relationships between various features, such as cost and rating, location and cuisines, etc. Visualize key insights through graphs and charts for better understanding. Tools and Libraries
The following libraries were used in the project:
Pandas: For data manipulation and analysis. NumPy: For numerical computations. Matplotlib: For basic plotting and visualization. Seaborn: For enhanced data visualization. Key Steps in the Analysis
Data Cleaning:
Handle missing values. Drop duplicates and irrelevant columns. Standardize categorical variables.
Data Exploration:
Univariate analysis of individual columns (e.g., distribution of ratings, average cost for two, etc.). Bivariate analysis to find relationships (e.g., correlation between rating and cost). Analysis based on different countries, cities, and cuisines.
Visualizations:
Distribution of restaurant ratings. Most popular cuisines. Restaurant cost distribution. Top cities with the highest number of restaurants. Correlation heatmaps to show relationships between variables.
Conclusion This project provides a comprehensive analysis of the Zomato dataset, highlighting key trends in restaurant ratings, cost, and customer preferences. The findings could be useful for understanding the dynamics of restaurant businesses and customer preferences across different regions.
Future Work Implementing machine learning models for predicting restaurant ratings or popularity based on features. Analyzing time-series data if available, to find trends over time. Adding more advanced visualizations and interactive plots using Plotly or Dash.