Predict Success of a Zomato Restaurant using Machine Learning

Abstract

Making an informed decision when choosing a restaurant has grown more challenging as the number of restaurants in Bangalore has increased. To assist in decision-making, websites like Zomato offer restaurant ratings and reviews. However, the quality of reviews can be subjective and biased. Therefore, predicting restaurant ratings can provide a more accurate representation of a restaurant’s quality and aid consumers in their decision-making process. This research paper aims to predict restaurant ratings in Bangalore using Zomato data. We will utilize machine learning methods like Linear Regression, Random Forest, and KNN to predict the rating of a restaurant. The study will also identify the features that contribute to higher ratings, such as location, price range, and type of cuisine. This research can assist both consumers in choosing the best restaurants and restaurant owners in identifying areas for improvement and increasing their ratings.