## 1 Introduction  
Prior researches show that human personality and favorite visiting place have considerable relationship. Coefficient of Determination is used in the relation between personality and favored locations [1]. By use of probability models such as Poisson distribution, the relation between personality and favored locations are identified and personal mobility model is predicted in [2, 3]. These are traditional methods of analysis based on statistics. A trail to use machine learning in order to these analysis can be found in [4] in terms of back propagation network. Nowadays, a lot of new methods including machine learning technologies can be adopted for such sort of analysis. In this research, we will show the relation between personal factors and favorite locations using various machine learning techniques, especially ensemble techniques, and will verify the consensus of results from machine learning methods. Ensemble techniques combines the result of each independent model and thus shows more preciseness comparing to single model only. By introducing up to date machine learning techniques, ensemble techniques of several machine learning models are used in this research. Two representative ensemble techniques are used: bagging and boosting. For bagging techniques, random forest is used since it is widely used. For boosting techniques, we used XGBoost since it has high performance and fast in training time, which is also widely used. Both of two methods, random forest and XGBoost have base model of decision tree. We also used stacking as shown in Section 2.4 in order to verify that various regression models other than decision tree also effective in our research in a form of meta learning. Different from previous researches, our focus is that common belief of relationship between personality and location selection is proved by state of the art technologies. As well personal features other than personality such as age, religion, method of transportation, salary, and so on, are also used for this relationship analysis. In addition, the results of ensemble methods will be presented in numerical manner. For the inputs of analysis, as well as personalities, other personal factors such as salary, method of transportation, religion, and so on are found to be related with favorite locations [5; 6]. However not all of these personal factors are meaningful factors for location preference. In other words, meaningless features for the input of analysis degrades the prediction accuracy of the relationship. Therefore feature selection [7] was executed for each location category. And then, prediction accuracy was improved through hyperparameter optimization. Hyperparameter optimization was done in three different ways: grid search, random search [8] and bayesian optimization [9] from the current advancement of AutoML. Grid search and random search are two representative methods of hyperparameter optimization. Grid search takes long time since it checks performance for every possible candidates of parameters. Random search is faster than grid search since it checks several samples randomly while shows less precision comparing to grid search. These two methods has shortages that current search information cannot be transferred to next step. Bayesian optimization overcomes this shortage: it utilizes prior knowledge for new search of optimized value with smaller search time and higher precision. In this research, all three of hyperparameter optimization methods are used. In this research, Big Five Factors (BFF) of personality such as Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism are used as well as the highest level of education, religion, salary, method of transportation, commute time, the frequency of journey in one year, social media usage status, time spent on social media per day, and nature of personaly hobby. BFF is taxonomy for personality characteristics presented by [PERSON] & McCrae in 1992, and it is found useful for personality related research.