:fire: Powerful, yet easy to use, open-source online ordering, table reservation and management system for restaurants

🍝 restaurant menu app made with flutter inspired by this design https://goo.gl/jChLBV

Food or Item Order Management System

Restaurant Management System with Website and POS. This system includes a customer-facing website, a POS system for ordering, and the ability to create, store, update, and delete bills, menu items, and more. Uses mainly php.

A responsive 3D menu concept for a restaurant website. The idea is to show the menu as a folded flyer and unfold it in order to show the menu items.

:iphone: :runner: :apple: Fitness application that’s used to keep track of your physical fitness data, daily calorie count, invite friends to work out together and ultimately get healthy.

Online Food Order App on React Native using Typescript. It is a hot tutorial series on youtube where you can learn how to make apps like Uber Eats from the sketch. We have covered all the advanced topics to make it production-ready.

Create digital menus effortlessly with this restaurant menu generator. Built on the T3 stack (Next.js pages router, Prisma, PlanetScale, TRCP, NextAuth) for seamless functionality

OrderWorder - Revolutionize your restaurant with a sleek, contactless full-stack app powered by AI. From QR code menus and smart chatbot recommendations to seamless ordering, real-time kitchen tracking, and powerful admin dashboards - all built with Next.js & SCSS for lightning-fast, modern dining experiences.

Persistently maintain multiple Chromecasts as digital signage without a browser.

Using Ionic and Firebase It's a Real-Time food court App where user can order food from different restaurants menu.

Laravel demo-project: manage restaurant menu with reordering positions. Partly generated with https://quickadminpanel.com

🍽️ Yandex Eats Clone built with Flutter. A fully-featured food delivery app replicating Yandex Eats UI/UX and functionality, including restaurant browsing, menu selections, order tracking, and more!

Restaurant Food Ordering Mobile Application

This is an Android application that can be used for providing a digital menu for a restaurant.

FriedShop : A e-commerce website built with ReactJS. This project is a front-end web application for a fictional fast-food restaurant called Friedshop, developed with ReactJS. It allows users to browse the menu, place orders, and track their status

Using Firebase, It's a Real-Time food delivery App where user can order food from different restaurants menu

Order Food Online. Manage Restaurants. Add, Delete or Edit Menu or Restaurants

A restaurant menu ordering application. Built with DoneJS in ES6.

:octocat: This is an Android application that can be used for providing a digital menu for a restaurant.

The Food Project is a web application built with Django that allows users to order food online from local restaurants. The project includes a menu of available dishes from the restaurant. On the backend, the project includes a Django admin panel that allows restaurant owners to manage their menus.

Piatto is a CSV to HTML Restaurant Menu Generator

‘FoodHub’ is a complete Food Ordering dynamic website which contains multiple restaurants menus, foods, details, locations and all other information which are directly controlled by different restaurant’s real owner.

Our project is restaurant management system, where we provide all the details that a manager needs to run the restaurant from ordering the food to generating bill and payment. In our project only an authorised person i.e. manager can login, He can see the data of customers and employee. He can also add, update or delete customer. We have table masters who assigns table to the customers and chef who prepares food, also main feature is we calculate the time taken to serve customer food after order is placed. And we also have preparation time for each food individually. Chef also gets bonus if he prepares food early than expected, and customers are given more discount if food is delivered to them late as reward for their patience. We allocate table based on members and table masters does that job, also customer can prebook their table. Also, we keep a record of the employee who have left their table, like which employee left and when. Menu is displayed based on categories, like food with same category are displayed together. We also have record of increase in the price or ratings, like if food price is increased then old price and new price, and also ratings of food, like which food is trending and hitting our restaurant or which food is not doing well And we generate bill automatically based on order items, and we have got many payment options like cash, card, paytm, etc.

The server-side application which serves the menu of the David Chu's China Bistro restaurant.

🏩 Django 🏪 Food 🕍 App 🕌 is a 🏰 full 🏤 featured 🏦 food 🏡 ordering 🚂 application 🚃 built 🏜 with 🚞 Django ✈ designed 🛩 to 🛫 simulate 🚁 a ⛴ real 🚟 world 🚠 restaurant 🚢 delivery 🚝 system 🧸 It ⛸ provides ⚽ functionality ⚾ from 🥎 menu 🏀 browsing 🏐 to 🎳 order 🎮 checkout 🎩 with 🔋 a clean 📘 UI 📗 backend 📙 scalable 📕 architectur

This repository is for a proposed Point of Sale system that might be beneficial in the hospitality space, particularly in restaurants.

# **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

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Free food delivery template built with Bootstrap & Gulp.