Our plan is to eradicate the gap between a food lover and a good food place. The quality and taste of food have been endangered. The Sindh Food Authority has failed to maintain food quality and taste in the industry. We can make our users go to a place where the user would pay for their food without
Foodie Recommender App
Our plan is to eradicate the gap between a food lover and a good food place. The quality and taste of food have been endangered. The Sindh Food Authority has failed to maintain food quality and taste in the industry. We can make our users go to a place where the user would pay for their food without any regrets. One can compromise quality in our culture but not the taste. We are here for no compromises on taste. Moreover, users will find and eat according to the preferences set on the app.
To collect data of user ratings on the food items of different restaurants in Karachi. To understand people’s reviews on different websites using NLP. To recommend restaurants and foods to users upon their preferences of food. In the future, it will also deliver food according to the current user’s location via the current location option (optional work)
Here are the Implementation %
per Members Data Gathering and Database/Cloud: Muhammad Sami 70%
Maaz Ahmed 20%
Muhammad Junaid Rizvi 10%
Front End UI/UX:
Muhammad Sami 15%
Maaz Ahmed 15%
Muhammad Junaid Rizvi 70%
Back End System:
Muhammad Sami 20%
Maaz Ahmed p
Muhammad Junaid Rizvi 10%
To provide better experience of best street food
To give the best response to foodie, when he wants to eat something good.
To get recognition of the street food with deserves to be recognized by people Make a review system to provide the best experience to our foodie
To make a recommendation system based upon the preferences of the foodie
Data Gathering and Database/Cloud: Web Scraping through Data Web Scraper. Selenium (optional) Dataset (.csv) or
Firebase Front End UI/UX: Mobile Application through Flutter.
Back End System: Artificial Intelligence. Recommendation System Through Machine Learning. Natural Language Processing.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| License Software's | Equipment | 2 | 10000 | 20000 |
| API'S | Equipment | 4 | 4570 | 18280 |
| Hosting Services | Equipment | 4 | 4020 | 16080 |
| Softboard, Stationary, Printing | Miscellaneous | 1 | 3000 | 3000 |
| Total in (Rs) | 57360 |
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