Optimizing Machine Learning in Hospitality Industry

This study explores the application of the Random Forest algorithm in predicting the length of stay (LoS) of hotel guests, a critical metric for optimizing operational efficiency and revenue management in the hospitality industry. The research is grounded in the growing need for predictive analytics to address challenges posed by fluctuating demand, diverse customer preferences, and dynamic market conditions. Accurate LoS predictions allow for better resource allocation, enhanced guest experiences, and optimized pricing strategies, making this study highly relevant for advancing data-driven decision-making in the sector. The methodology involved analyzing a dataset of 453 accounts, which included key features such as ratings, guest types, room preferences, and country of origin. Comprehensive data preprocessing steps, including standardization, feature selection, and dataset splitting into training and testing subsets, ensured the reliability and robustness of the predictive model. The Random Forest algorithm, known for its ability to handle non-linear relationships and high-dimensional data, was implemented to analyze patterns and relationships. The model demonstrated high accuracy, achieving a Mean Squared Error (MSE) of 1.89, Mean Absolute Error (MAE) of 0.80, and Root Mean Squared Error (RMSE) of 1.37, effectively capturing the complexity of the dataset. The findings reveal that ratings and guest types are the most influential predictors, underscoring their importance in shaping guest behaviors. While the results are promising, limitations such as dataset size and scope suggest opportunities for further research. Future studies could incorporate more extensive, diverse datasets and explore alternative algorithms to enhance predictive accuracy and adaptability. This research contributes to advancing machine learning applications in hospitality, providing actionable insights to improve operational performance, guest satisfaction, and competitive positioning.