A Machine Learning Approach to Predicting Vacation Choices Based on Demographic and Lifestyle Factors

Planning a vacation is not easy and choosing a destination is itself a difficult task. But with modern machine learning technology we can predict user preferences and recommend suitable destinations for vacations. This research aims to analyze public preferences between two popular vacation destinations named mountains and beaches, using ma- chine learning techniques. By considering demographic factors like age, gender, income, education and lifestyle choices, this study explores the influences on vacation destination preferences. A unique dataset containing over 52,000 instances is used to predict whether individuals prefer mountains or beaches, employing algorithms like Decision Tree, Random Forest, Gradient Boosting, Deep Learning, and Ensemble Methods. The study concludes that Deep Learning models achieved the highest accuracy of 99.81%, followed by Gradient Booster at 98.85%. The results suggest that machine learning can enhance personalized travel recommendations and contribute to more efficient tourism marketing.