The purpose of this paper is to apply state-of-the-art machine learning techniques for assessing the quality of the restaurants using restaurant inspection data. The machine learning techniques are applied to solve the real-world problems in all sphere of life. Health and food departments pay regular visits to restaurants for inspection and mark the condition of the restaurant on the basis of the inspection. These inspections consider many factors that determine the condition of the restaurants and make it possible for the authorities to classify the restaurants.
In this paper, standard machine learning techniques, support vector machines, naïve Bayes and random forest classifiers are applied to classify the critical level of the restaurants on the basis of features identified during the inspection. The importance of different factors of inspection is determined by using feature selection through the help of the minimum-redundancy-maximum-relevance and linear vector quantization feature importance methods.
The experiments are accomplished on the real-world New York City restaurant inspection data set that contains diverse inspection features. The results show that the nonlinear support vector machine achieves better accuracy than other techniques. Moreover, this research study investigates the importance of different factors of restaurant inspection and finds that inspection score and grade are significant features. The performance of the classifiers is measured by using the standard performance evaluation measures of accuracy, sensitivity and specificity.
This research uses a real-world data set of restaurant inspection that has, to the best of the authors’ knowledge, never been used previously by researchers. The findings are helpful in identifying the best restaurants and help finding the factors that are considered important in restaurant inspection. The results are also important in identifying possible biases in restaurant inspections by the authorities.
