Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality: Dataset from the Indian Hotel Industry

The present study endeavours to ascertain whether all the dimensions of SERVQUAL carry equal weight in terms of their impact on overall service quality. Questions were framed to measure each of the dimensions of SERVQUAL with different predictor variables: gender, age, marital status, highest level of education, and frequency of staying at the hotel. Machine learning models are used to find the importance of each feature against a dimension. Comparative modeling of feature importance was done using the CatBoost gradient boosting technique and Microsoft Azure Automated Machine Learning studio. The result confirmed an excellent similarity between CatBoost's drawn feature importance and those obtained from Azure auto ML studio. The results of the investigation enable decision-makers to determine which dimension is dependent on specific predictors, allowing them to focus on targeted improvements.