Exploratory Data Analysis (EDA) of Zomato Dataset

Introduction: This project entails conducting Exploratory Data Analysis (EDA) on the Zomato dataset, focusing on key attributes such as Restaurant ID, Restaurant Name, Country Code, City, Address, Locality, Cuisine, Average Cost for two, and more. Through this analysis, we aim to gain insights into the restaurant industry, consumer preferences, and market trends.

Dataset Overview: The Zomato dataset encompasses various attributes related to restaurants, including their IDs, names, locations (country, city, address, locality), geographical coordinates (longitude, latitude), cuisines offered, average cost for two, currency used, booking and delivery options, price range, aggregate rating, rating color and text, and the number of votes received.

Objective: The primary objectives of this EDA project are:

Understand the distribution of restaurants across different countries, cities, and localities. Analyze the variety of cuisines offered and their popularity. Investigate the relationship between average cost for two and aggregate rating. Explore the availability of table booking and online delivery options. Examine the impact of price range on ratings and consumer preferences. Methodology:

Data Preprocessing: Handling missing values. Cleaning data for consistency (e.g., removing duplicates, correcting errors). Feature engineering if required (e.g., extracting country-wise information from the address). Exploratory Data Analysis: Descriptive statistics: Calculating measures of central tendency and dispersion for numerical features. Visualizations: Utilizing histograms, bar charts, scatter plots, and heatmaps to visualize distributions, correlations, and patterns. Grouping and aggregation: Analyzing data based on categorical variables like country, city, and cuisine. Insights Generation: Identifying trends: Recognizing patterns in ratings, cost, and popularity of cuisines. Consumer preferences: Understanding which cuisines, locations, or restaurant types are favored by customers. Correlation analysis: Investigating relationships between variables to draw meaningful conclusions. Tools Used:

Programming Language: Python Libraries: Pandas, NumPy, Matplotlib, Seaborn for data manipulation and visualization. Expected Outcome: By the end of this project, we anticipate:

Comprehensive insights into the Zomato dataset, highlighting trends and patterns in the restaurant industry. Understanding of consumer preferences and factors influencing ratings and popularity. Actionable recommendations for restaurant owners, Zomato platform managers, and stakeholders based on the analysis. Conclusion: Exploratory Data Analysis of the Zomato dataset provides valuable insights into the restaurant landscape, consumer behavior, and market dynamics. Through descriptive statistics, visualizations, and analytical techniques, this project aims to uncover patterns and correlations that can inform decision-making and strategy formulation in the food service industry.