Food Recommendation System using Data Mining and SVM Classification using java



ABSTRACT:

Food Ordering System is an application which will help user to order food in online. Restaurant dataset is collected for predicting the top restaurants. Food Ordering System reduces manual works and improves efficiency of restaurant. In this project we have implemented the SVM(Support Vector Machine) Algorithm to classify the dataset based on location, cuisine and price. Two type of location based search has been implemented, first is location and feedback and second search is location, cuisine and price. Higher priority is given to the restaurants which are having good reviews, delivery , veg and non veg. Then the lower priority restaurants also displayed below the higher priority restaurants. The restaurants which are having good or positive review will be displayed first and rest of them will be displayed vice versa. Here user can choose the restaurant and book items. Collaborative filtering algorithm is proposed to classify the feedbacks into positive and negative feedbacks.
 
EXISTING SYSTEM:  

Many Restaurants stores and maintain their day to day transactions manually. But some of them are having automation system which is helping them to store the data. But such restaurants are storing the information about the orders and the customer information. They don’t have facility to store the information of feedbacks and favourite orders of customers over some period of time. Restaurants are having standalone applications so at one time, they have the facility of many screens or many operations which is happening at one time. So they are storing them and then at last, the restaurant managers will able to see the data of last day.
The software which restaurants are using is very costly and their maintenance which is very high. The software which we tried and get the information was called “Food Delivery”. This software which free for download but the restaurant has to pay after sometime. Command buttons for viewing and making reservations line the top of the window, along with buttons for viewing customer history and reports. We were able to jump right in and quickly create a new reservation 

DRAWBACKS:

This software is basically used only for reservation means table booking. So if we want to just order some food or store any feed backs then it won’t.
At last the restaurants have to store by themselves which will became no use of software.
The user interface of the application is also not that much attractive. 
So from the restaurants point of view, they are able to store only one kind of information. There is no security feature also. 
This will creates lot of mistakes like misspellings, calculation problems, duplicate entries etc.
PROPOSED SYSTEM:

As this online application enables the end users to register to the system online, select the food items of their choice from the menu list, and order food online. Also the payment can be made through online mode or at the time of home delivery depending upon the customer’s choice and convenience. 
It defines the payment to be done by the customer for order placed from the web store at worth price. 
We proposed SVM algorithm used to classify the dataset into location, price, cuisine, veg or non-veg, delivery and feedback. To analyze the sentiment, we have proposed the collaborative filtering algorithm. 
This algorithm is used to detect the positive and negative feedback. The user given feedback is checked with positive and negative dataset by collaborative filtering algorithm. 

ADVANTAGES:

These interjections are all too familiar for anyone who takes orders over the phone. Occasionally, a misunderstanding occurs or an employee takes down the wrong order. Cue the angry customers, wasted food, and disappointed manager. With online orders, the customer makes everything clear on their end. Everything is in online, and there s no mix up. 
There is no application which recommends restaurant based on Feedback
Location Based Search is implemented and it is very easy to identify the nearby restaurants.

ALGORITHM USED:

Support Vector Machine (SVM).
Collaborative Filtering Algorithm.
SYSTEM ARCHITECTURE:

SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
System : Pentium IV 2.4 GHz.
Hard Disk        : 40 GB.
Floppy Drive : 1.44 Mb.
Monitor : 15 VGA Colour.
Mouse : Logitech.
Ram : 512 Mb.
SOFTWARE REQUIREMENTS:
Operating system : Windows XP.
Coding Language :  JAVA
Data Base :  MYSQL

REFERENCES:

Wubin Guo, Xianpei Hu, Kuanjiu Zhou, Lijun Sun, "An Intelligent Query System Based on Chinese Short Message Service for Restaurant Recommendation", Sixth International Conference on the Management of Mobile Business (ICMB 2007), pp. 1, 2007.
 


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