## Modeling Job Satisfaction of Peruvian Basic Education Teachers Using Machine Learning Techniques  
[PERSON] 1,*[1], [PERSON] 2[2], [PERSON] 3[3],  
[PERSON] 1[1], [PERSON] 1[1], [PERSON] 4[5],  
[PERSON] 4[6], [PERSON] 5[7], [PERSON] 6[8] and [PERSON] 7[10]  
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students, the school environment and the educational system in general [3; 4]. It is currently known that satisfaction or dissatisfaction of the teachers with their work life could have a determining impact on the quality of teaching and student achievement [5; 6; 7; 8]. In Peru, according to the Ref. [9], 42.1% of teachers have a regular level of satisfaction, that is, they have a low positive attitude about their work. In this scenario, it is of utmost importance to conduct studies to better understand the job satisfaction of teaching professionals, using current methods and tools. In line with this idea, the Ref. [10] recommends conducting studies that allow the analysis of how to avoid the lack of satisfaction in order to favor the non-appearance of burnout and demotivating symptoms in teachers. Studies close to this problem have been employing machine learning techniques, such as logistic regression, random forests, decision trees, AdaBoost algorithm, K-Nearest Neighbors, support machines, Naive Bayes with great success in the prediction of employee desertion in several organizations [11; 12; 13]. These techniques are also being employed to explore and discover reliable predictors in large volumes of data, with a large number of dimensions [14]. It is also possible to find factors affecting employee job satisfaction using Gradient boosting and Random Forests algorithms [15]. Another study developed by the [PERSON]. [16] demonstrates the use of artificial neural networks to find the association between demographic variables, coaching leadership and job satisfaction of elementary school teachers in Korea with better results than multiple regression. Prediction of employee job satisfaction was also addressed using deep neural networks [17], decision tree algorithm (CART) and neural networks [18]. Although there are important advances in this area, most of them address job satisfaction in a more general way, on the other hand, there are studies that attempt to explain teacher job satisfaction using techniques such as linear hierarchical models [19], structural equation modeling [20], and analysis of variance (ANOVA) [21]. The contributions of this research are:  
* We determined that the XGBoost and Random Forest algorithms allow us to obtain predictive models of job satisfaction of Peruvian basic education teachers with a balanced accuracy of 74%, sensitivity of 74%, F1-Score of 0.48, negative predictive value of 0.94, higher values of true-positives (479 instances) and lower values of false-negatives (168 instances). These values are unprecedented in the prediction of job satisfaction of basic education teachers in Peru.