# **ABSTRACT** Main Objective: The main agenda of this project is: Perform extensive Exploratory Data Analysis(EDA) on the Zomato Dataset. Build an appropriate Machine Learning Model that will help various Zomato Restaurants to predict their respective Ratings based on certain features DEPLOY the Machine learning model via Flask that can be used to make live predictions of restaurants ratings A step by step guide is attached to this documnet as well as a video explanation of each concpet. Zomato is one of the best online food delivery apps which gives the users the ratings and the reviews on restaurants all over india.These ratings and the Reviews are considered as one of the most important deciding factors which determine how good a restaurant is. We will therefore use the real time Data set with variuos features a user would look into regarding a restaurant. We will be considering Banglore City in this analysis. Content The basic idea of analyzing the Zomato dataset is to get a fair idea about the factors affecting the establishment of different types of restaurant at different places in Bengaluru, aggregate rating of each restaurant, Bengaluru being one such city has more than 12,000 restaurants with restaurants serving dishes from all over the world. With each day new restaurants opening the industry has’nt been saturated yet and the demand is increasing day by day. Inspite of increasing demand it however has become difficult for new restaurants to compete with established restaurants. Most of them serving the same food. Bengaluru being an IT capital of India. Most of the people here are dependent mainly on the restaurant food as they don’t have time to cook for themselves. With such an overwhelming demand of restaurants it has therefore become important to study the demography of a location. What kind of a food is more popular in a locality. Do the entire locality loves vegetarian food. If yes then is that locality populated by a particular sect of people for eg. Jain, Marwaris, Gujaratis who are mostly vegetarian. These kind of analysis can be done using the data, by studying the factors such as • Location of the restaurant • Approx Price of food • Theme based restaurant or not • Which locality of that city serves that cuisines with maximum number of restaurants • The needs of people who are striving to get the best cuisine of the neighborhood • Is a particular neighborhood famous for its own kind of food. “Just so that you have a good meal the next time you step out” The data is accurate to that available on the zomato website until 15 March 2019. The data was scraped from Zomato in two phase. After going through the structure of the website I found that for each neighborhood there are 6-7 category of restaurants viz. Buffet, Cafes, Delivery, Desserts, Dine-out, Drinks & nightlife, Pubs and bars. Phase I, In Phase I of extraction only the URL, name and address of the restaurant were extracted which were visible on the front page. The URl's for each of the restaurants on the zomato were recorded in the csv file so that later the data can be extracted individually for each restaurant. This made the extraction process easier and reduced the extra load on my machine. The data for each neighborhood and each category can be found here Phase II, In Phase II the recorded data for each restaurant and each category was read and data for each restaurant was scraped individually. 15 variables were scraped in this phase. For each of the neighborhood and for each category their onlineorder, booktable, rate, votes, phone, location, resttype, dishliked, cuisines, approxcost(for two people), reviewslist, menu_item was extracted. See section 5 for more details about the variables. Acknowledgements The data scraped was entirely for educational purposes only. Note that I don’t claim any copyright for the data. All copyrights for the data is owned by Zomato Media Pvt. Ltd.. Source: Kaggle

Zomato Data Exploration and Analysis with SQL (SQL SERVER)

The basic idea of analyzing the Zomato dataset is to get a fair idea about the factors affecting the aggregate rating of each restaurant, establishment of different types of restaurant at different places, Bengaluru being one such city has more than 12,000 restaurants with restaurants serving dishes from all over the world. With each day new restaurants opening the industry has’nt been saturated yet and the demand is increasing day by day. Inspite of increasing demand it however has become difficult for new restaurants to compete with established restaurants. Most of them serving the same food. Bengaluru being an IT capital of India. Most of the people here are dependent mainly on the restaurant food as they don’t have time to cook for themselves. With such an overwhelming demand of restaurants it has therefore become important to study the demography of a location. What kind of a food is more popular in a locality. Do the entire locality loves vegetarian food. If yes then is that locality populated by a particular sect of people for eg. Jain, Marwaris, Gujaratis who are mostly vegetarian. These kind of analysis can be done using the data, by studying different factors.

This project involves the analysis of the Zomato dataset for restaurants in Bengaluru city. The dataset provides information about various restaurants, including their ratings, cuisines, costs, and more. Through this analysis, we aim to gain insights into the restaurant landscape in Bengaluru and explore factors that influence ratings.

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This project performs Exploratory Data Analysis (EDA) on a Zomato dataset, focusing on data cleaning, processing, and visualization. The aim is to derive meaningful insights about restaurant ratings, locations, cuisines, and more.

Analyzing the zomato restaurant dataset using python libraries

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The main goal of this project is to perform extensive Exploratory Data Analysis(EDA) on the Zomato Dataset and build an appropriate Machine Learning Model that will help various Zomato Restaurants to predict their respective Ratings based on certain features.

Exploratory data analysis of datasets available in Kaggle.(IPL dataset, Zomato dataset, Loan dataset, Telecom customer churn dataset)

Performed the exploratory data analysis of Zomato dataset using Oracle SQL.

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Zomato hotels analysis using zomato-bangalore-restaurants from kaggle,used python numpy,pandas,seaborn,matplotlib

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Performed Exploratory data analysis on zomato's restaurants' dataset.

Exploratory data analysis of Zomato Restaurants dataset using Python

Exploratory data analysis of Zomato dataset using Postgress SQL.

This repository contains an Exploratory Data Analysis (EDA) on the Zomato dataset, which contains information about restaurants, their ratings, and customer reviews in India. The dataset was obtained from Kaggle and contains data from various cities in India.

This project performs an exploratory data analysis (EDA) on the Zomato dataset to uncover insights about restaurants, cuisines, ratings, and locations.

Exploratory data analysis of Zomato restaurant dataset using Python with Pandas, NumPy, Matplotlib, and seaborn to explore restaurant type, rating, cost trend, and online ordering behavior.

The main goal of this project is to perform extensive Exploratory Data Analysis(EDA) on the Zomato Dataset and build an appropriate Machine Learning Model that will help various Zomato Restaurants to predict their respective Ratings based on certain features.

A data analysis project that explores Zomato’s restaurant dataset using Python to uncover trends in ratings, pricing, and service features such as online ordering and table booking.

In this project, I have analysed a dataset from Kaggle about Zomato's different restaurant branches using Python and its data analysis libraries like Pandas, Numpy, Seaborn and Matplotlib.

Conducted data collection and preprocessing of the Zomato dataset for comprehensive analysis. Performed exploratory data analysis (EDA) to identify patterns and anomalies within the data. Developed data visualizations and applied statistical methods to extract meaningful insights.

This repository contains an Exploratory Data Analysis (EDA) on the Zomato dataset, which contains information about restaurants, their ratings, and customer reviews in India. The dataset was obtained from Kaggle and contains data from various cities in India.

Welcome to my data analysis project focused on Zomato, one of India's leading food delivery platforms. This project uses Python to explore customer behavior, restaurant ratings, and ordering patterns based on a real-world dataset. The goal is to extract actionable insights through Exploratory Data Analysis (EDA) and visualization.

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Exploratory Data Ananlysis of Tomato Dataset

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