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Hotel Booking Cancellations Prediction Using Machine Learning Techniques for Optimized Management
Reservation cancellations are a massive issue in the hospitality sector as they disrupt the demand forecasts and cost businesses away 20 percent of their revenues. You must be capable of making proper predictions regarding cancellations in order to optimize the operations, price and resource management of your hotel. The dataset that we used was Hotel Booking Demand, with 32 characteristics of the hotel of a resort and city. These characteristics consist of categorical and numerical data regarding customer booking and their type of customer and the specifics of their booking. Preprocessing involved a process of dealing with missing values, eliminating duplicate values and the process of evening out the distribution of classes using the RandomUnderSampler. Some of the classification techniques employed are LR, DT, RF, XGBoost, Gradient Boosting, LGBMClassifier, SVM, and MLP. The hyperparameters were fine-tuned with the help of gridSearchCV. The precision of the forecasts was enhanced through Stacking Classifier where the major model consisted of XGBoost, Random Forest, Gradient Boosting, and Logistic Regression. Categorical features were further encoded as labels to make them even more accurate with the help of Label Encoding and RFE was utilized to identify the most significant factors that influenced cancellations. The Voting Classifier which used a combination of Decision Tree and MLP models increased the accuracy to 98.7%. The importance of each feature was also determined using XAI tools such as LIME and SHAP. Flask-based user interface enabled real time and live prediction to aid good decisions by the hotel management systems.
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