Background This paper explores machine learning algorithms and approaches for predicting alum income to obtain insights on the strongest predictors and a ‘high’ earners’ class. Methods It examines the alum sample data obtained from a survey from a multicampus Mexican private university. Survey results include 17,898 and 12,275 observations before and after cleaning and pre-processing, respectively. The dataset comprises income values and a large set of independent demographical attributes of former students. We conduct an in-depth analysis to determine whether the accuracy of traditional algorithms can be improved with a data science approach. Furthermore, we present insights on patterns obtained using explainable artificial intelligence techniques. Results Results show that the machine learning models outperformed the parametric models of linear and logistic regression, in predicting alum’s current income with statistically significant results (p < 0.05) in three different tasks. Moreover, the later methods were found to be the most accurate in predicting the alum’s first income after graduation. Conclusion We identified that age, gender, working hours per week, first income and variables related to the alum’s job position and firm contributed to explaining their current income. Findings indicated a gender wage gap, suggesting that further work is needed to enable equality.

Wind turbines are often placed in complex terrains, where benefits from orography-related speed up can be capitalized. However, accurately modeling the wind resource over the extended areas covered by a typical wind farm is still challenging over a flat terrain, and over a complex terrain, the challenge can be even be greater. Here, a novel approach for wind resource modeling is proposed, where a linearized flow model is combined with a machine learning approach based on the k-nearest neighbor (k-NN) method. Model predictors include combinations of distance, vertical shear exponent, a measure of the terrain complexity and speedup. The method was tested by performing cross-validations on a complex site using the measurements of five tall meteorological towers. All versions of the k-NN approach yield significant improvements over the predictions obtained using the linearized model alone; they also outperform the predictions of non-linear flow models. The new method improves the capabilities of current wind resource modeling approaches, and it is easily implemented.

Background: This paper explores different machine learning algorithms and approaches for predicting alum income to obtain insights on the strongest predictors for income and a ‘high’ earners’ class. Methods: The study examines the alum sample data obtained from a survey from Tecnologico de Monterrey, a multicampus Mexican private university, and analyses it within the cross-industry standard process for data mining. Survey results include 17,898 and 12,275 observations before and after cleaning and pre-processing, respectively. The dataset includes values for income and a large set of independent variables, including demographic and occupational attributes of the former students and academic attributes from the institution’s history. We conduct an in-depth analysis to determine whether the accuracy of traditional algorithms in econometric research to predict income can be improved with a data science approach. Furthermore, we present insights on patterns obtained using explainable artificial intelligence techniques. Results: Results show that the gradient boosting model outperformed the parametric models, linear and logistic regression, in predicting alum’s current income with statistically significant results (p < 0.05) in three tasks: ordinary least-squares regression, multi-class classification and binary classification. Moreover, the linear and logistic regression models were found to be the most accurate methods for predicting the alum’s first income. The non-parametric models showed no significant improvements. Conclusion: We identified that age, gender, working hours per week, first income after graduation and variables related to the alum’s job position and firm contributed to explaining their income. Findings indicated a gender wage gap, suggesting that further work is needed to enable equality.