Identifying Key Features in Yelp Data for Success in Different Types of Restaurants
DOI:
https://doi.org/10.29407/intensif.v9i1.23476Keywords:
Yelp Dataset Challenge, Linear Square Regression, Restaurant AttributesAbstract
Background: The purpose of this research is to measure of customer satisfaction for newly established independent restaurants and, consequently, good predictors of independent restaurant success. Urban communities face several challenges, including how to best use scarce resources like real estate and support small enterprises. Smart businesses are essential to the development of smart cities because they use data analytics to inform their strategic planning and design choices, and the target of this topic is restaurant. Objective: Restaurants control a sizable portion of the city market's small business sector. As part of the Yelp Data Challenge, Yelp just made available an open dataset that includes important details, ratings, and Yelp scores for every restaurant in different cities. Methods: Our methodology utilizes a vector of crucial factors to accurately forecast a business’s prospective success and exclusively evaluate eateries located inside the city limits of Las Vegas. The dependent variables will consist of the mean Yelp ratings for each restaurant and constructed our model by following the subsequent stages. Conclusion: The findings of this research is corroborated by the discovery that the statistically significant properties of restaurants, shown by a low p-value, varied across various restaurant categories, the unique modeling technique to forecast future restaurants' Yelp rankings based on their design choices. This will assist owners of restaurants in making better design choices, which will result in more prosperous small enterprises in urban settings.
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