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Culinary Analytics: Leveraging Machine Learning for Anticipating Restaurant Reviews and Ratings

Afsha Firdouse, Fouziya ., Ameena Begum, Mehwish khan, M. Bharathi, T. Aditya Sai Srinivas

Abstract


In the dynamic realm of dining choices, Restaurant Reviews and Ratings are pivotal, guiding consumers in their quest for a satisfying meal. Fueled by the rapid growth of social platforms, the influx of consumer reviews necessitates an efficient means for users to swiftly comprehend descriptors across digital networks. This paper delves into the realm of restaurant reviews and ratings, employing Python-based Machine Learning techniques. Analyzing a diverse dataset inclusive of reviews, locations, and preferences, the article utilizes Random Forest and Decision Tree algorithms. Rigorous evaluation with metrics like MSE, RMSE, and R2 score showcases the model's accuracy, excelling in reaction analysis and rating predictions.


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References


https://github.com/Nayana46/Restaurant-Rating-Prediction

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