Enhancing Decision-Making in Restaurant Selection: Predictive Modeling and Business Intelligence for Zomato Reviews

DOI

No DOI

Published In

Volume 5, Issue 2 (2024)

Published Online

March-April 2024

Pages

178-185

Article Type

Research Article

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Abstract: In this paper, We want to predict restaurant reviews on Zomato platform using machine learning based classification algorithms. Zomato is a popular online platform for discovering and reviewing restaurants. Using a rich dataset from Zomato that includes features such as location, cuisine type, price range, and user reviews, We aims to build a predictive model that can accurately classify restaurants into review categories. This paper includes data preprocessing, feature engineering, model selection and evaluation to determine the most efficient classification algorithm. The use of predictive modeling and business intelligence to improve decision-making in restaurant selection on Zomato, a popular online review platform. The authors leverage Zomato's rich user- generated review data to develop a model that can predict the helpfulness of reviews. Using these inputs, the provided model predicts the restaurant's rating based on the features provided. The interface is intuitive and easy to use. Users simply enter relevant information about the restaurant they are interested in and the app quickly creates a forecast. Additionally, the interface may contain visualizations or informative displays to improve the user's understanding of the forecasting process. Our paper provide zomato users with valuable information to make effective nutritional decisions.