ZOMATO RESTRAURENT DATA ANALYSIS

The rapid growth of online food delivery platforms has generated vast amounts of data that can be leveraged to
gain valuable business insights. This project focuses on analyzing restaurant data from Zomato to understand
customer preferences, restaurant performance, and market trends. The dataset includes key attributes such as
restaurant location, cuisines offered, pricing, ratings, and online delivery availability Through exploratory data
analysis (EDA), data visualization, and statistical techniques, the study identifies patterns and relationships
between various factors influencing customer choices. The analysis highlights how location, cost, and cuisine type
affect restaurant ratings and popularity. Additionally, the project examines the impact of online delivery services
and table booking options on customer engagement The findings from this analysis can assist restaurant owners,
food delivery platforms, and stakeholders in making data-driven decisions to improve service quality, optimize
pricing strategies, and enhance customer satisfaction. Overall, this project demonstrates the importance of data
analytics in the food service industry and its role in driving business growth. The project titled “Zomato Restaurant
Data Analysis” focuses on understanding the restaurant industry through the lens of data analytics by leveraging
the publicly available Zomato dataset. Food delivery and restaurant discovery platforms have revolutionized the
dining experience in India and across the globe, and Zomato is among the leading players in this sector. The
dataset contains valuable information such as restaurant names, locations, cuisines offered, price ranges, ratings,
votes, and delivery options. By performing a detailed analysis of this data, the project aims to uncover hidden
patterns, customer preferences, business trends, and factors that contribute to the success or failure of restaurants.
The primary motivation behind this work is the rapidly growing demand for data-driven insights in the food and
hospitality sector, where decisions about pricing, menu design, marketing, and customer engagement can no
longer rely solely on intuition but must be backed by analytics .
The project begins with data collection and preprocessing. Since the dataset often comes with missing values,
inconsistent labels, and noisy entries, the initial step is to clean the data using Python libraries such as Pandas and
NumPy. Standardization of categorical columns like cuisines and cities ensures that the analysis is meaningful.
Data visualization tools such as Matplotlib, Seaborn, and Plotly are employed to generate intuitive plots that help
us interpret the dataset effectively. Exploratory Data Analysis (EDA) is the backbone of the project, where
descriptive statistics reveal the general trends of restaurant distribution, popular cuisines, and cost ranges. The
dataset is then studied to answer specific questions: which cuisines are most popular among customers, which
cities host the highest number of restaurants, whether cost impacts ratings, and how online delivery influences
restaurant popularity