Editor/Reviewer loginFrequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
IJCATR Volume 8 Issue 9
Restaurants Rating Prediction using Machine Learning Algorithms
Atharva Kulkarni, Divya Bhandari, Sachin Bhoite
10.7753/IJCATR0809.1008
keywords : Pre-processing, EDA, SVM Regressor, Linear Regression, XGBoost Regressor, Boosting.
Restaurant Rating has become the most commonly used parameter for judging a restaurant for any individual. A lot of research has been done on different restaurants and the quality of food it serves. Rating of a restaurant depends on factors like reviews, area situated, average cost for two people, votes, cuisines and the type of restaurant. The main goal of this is to get insights on restaurants which people like visit and to identify the rating of the restaurant. With this article we study different predictive models like Support Vector Machine (SVM),Random forest and Linear Regression, XGBoost, Decision Tree and have achieved a score of 83% with ADA Boost.
@artical{a892019ijcatr08091008,
Title = "Restaurants Rating Prediction using Machine Learning Algorithms",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "8",
Issue ="9",
Pages ="375 - 378",
Year = "2019",
Authors ="Atharva Kulkarni, Divya Bhandari, Sachin Bhoite"}
The paper gives detailed information of the Restaurant’s rating system contingent upon its attributes.
Selection of relevant attributes through an extensive study of the system.
Detailed Exploratory Data Analysis helps to not only understand the attributes and their relations but also analyze key findings from the data.
Implementation of a number of Supervised Machine Learning algorithms and upgrading the performance using Boosting algorithms.