The SwiggyUI app is a user interface replica of the popular food delivery platform - Swiggy, developed using Flutter

espresso

Scratch card reveal effect inspired by Google pay, PhonePe and Zomato scratch card reward system.

Low Level Design of Food delivery app - Zomato, Swiggy, UberEats

Zomato Restaurants Exploratory Data Analysis, Visualization and Prediction with Sentiment Analysis of Reviews and Recommendation System

This project uses Zomato API to get restaurants,recipes,ratings,etc.Screenshots attached

A clone of Zomato, A Online Food Ordering App using Jetpack Compose

Zomato Clone - Food Delivery Application Clone using React JS. In this Zomato App clone I have cloned maximum pages Home page, User Profile page, Food Selection page, Settings page etc... As it is only frontend clone I have added static data but in a dynamic way so data can be changed easily or added easily.

Jomato is a unofficial api wrapper for zomato

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Flutter Zomato App UI Code Tutorial 😅✌️

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A web clone of most used food order website Zomato

A Scrapy project for scraping restaurant information from zomato.com

This is demo code for LLD Bootcamp. It is not intended to be production level code and can be improved, but it sets the thought process and provides examples for how classes can be structured in a food delivery app like Swiggy or Zomato. process

Contains the coursera capstone project

clone of zomato

# **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

:hamburger: CLI for showing basic stats of Zomato orders

Android application build with MVVM Pattern, using Zomato API to enable search cities arround the world and display the city restaurants on a map.

Zomato Data Exploration and Analysis with SQL (SQL SERVER)

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Restaurant Bot using Zomato API using RASA Bot framework

This project demonstrates a real-time delivery location tracking system similar to Zomato/Swiggy, built using Spring Boot and Apache Kafka.

iOS like Activity Indicator for Zomato orders on Android

Zomato landing page clone with HTML and CSS

A script for Zomato. Automates the process of finding the best restaurant nearby, and also finds the restaurants that offer the best deals for a particular dish.

An advanced Streamlit-powered analytics dashboard built on the Zomato dataset featuring 20+ interactive tabs, 100+ metrics, and deep insights into cuisines, cities, prices, and restaurant behavior. From data cleaning to persona segmentation, it transforms raw food data into a visual, story-driven experience.

A clone of new zomato UI in flutter