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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM34630

ZOMATO RESTAURANT RATING PREDICTION

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM34630

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

SO

SHIVANSH OJHA

SS

SANKET SAXENA

DSR

DR SADHANA RANA

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Abstract

Zomato, a leading food review platform, was founded in 2008 in India and has now expanded to 24 countries globally. The term has become so popular in the culinary world that it is frequently used as a verb in everyday conversations. Phrases like “Have you tried this restaurant?” The phrase “Let’s Zomato it” captures the widespread impact of Zomato on people’s dining preferences. Restaurant evaluations heavily influence people’s dining choices. They reflect the establishment’s quality, cleanliness, and ambiance, affecting customer decisions.Higher ratings (stars or numerical values ranging from 1 to 5) indicate better profitability. Zomato has revolutionized restaurant discovery and selection, significantly changing the dining experience. Zomato has transformed restaurant discovery. It has helped customers identify affordable dining options. The Zomato restaurant rating prediction project improves the eating experience for users and offers useful data to restaurant owners. This project combines data science, machine learning, and web development to improve eating experiences. By precisely forecasting restaurant ratings, users can make data-driven decisions to ensure their dining experience matches their preferences. This project simplifies restaurant choices, saving consumers time and effort. Restaurant operators profit from this research since it provides valuable insights into the aspects that influence ratings. With this knowledge, businesses can make focused modifications to boost ratings and profitability.

KEYWORDS

Pre-processing Logistic Regression XGBoost Naiye Bayes KNN Decision Tree

Cite This Article

SHIVANSH OJHA, SANKET SAXENA, DR SADHANA RANA, "ZOMATO RESTAURANT RATING PREDICTION," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024.

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Publication Date

5/26/2024

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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