Enhancing Hotel Rating Predictions Through Machine Learning: Data Analytics Applications in Indian Hospitality and Digital Marketing

Abstract

With more consumers relying on online reviews, predicting hotel ratings accurately has become very important. This study investigates the use of machine learning models to predict overall hotel ratings based on key service-related features, including location, hospitality, cleanliness, facilities, food, value for money, and price. Using a real-world dataset of Indian hotels, we evaluate and compare the performance of six supervised learning models: Linear Regression, Random Forest, Gradient Boosting, Support Vector Regression, K-nearest neighbours, and PCA-based Linear Regression. The models were evaluated using Mean Squared Error (MSE) and R-squared (R²) as performance metrics. Gradient Boosting demonstrated the highest predictive accuracy, closely followed by Random Forest. Feature importance analysis identified hospitality, cleanliness, and location as the most significant predictors of customer satisfaction. Principal Component Analysis (PCA) further reduced dimensionality while retaining over 90% of the dataset's variance within the first four components. These findings demonstrate the effectiveness of ensemble learning methods for hotel rating prediction and offer actionable insights for service improvement in the Indian hospitality sector. Furthermore, the results underscore the role of data-driven analytics in shaping effective digital marketing and promotional strategies tailored to diverse customer preferences.