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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Results show that the machine learning models outperformed the parametric models of linear and logistic regression, in predicting alum\u2019s current income with statistically significant results (p &lt; 0.05) in three different tasks. Moreover, the later methods were found to be the most accurate in predicting the alum\u2019s first income after graduation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We identified that age, gender, working hours per week, first income and variables related to the alum\u2019s 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40537-022-00559-6","type":"journal-article","created":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T08:10:05Z","timestamp":1643011805000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Supervised machine learning predictive analytics for alumni income"],"prefix":"10.1186","volume":"9","author":[{"given":"Daniela A.","family":"Gomez-Cravioto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7324-7205","authenticated-orcid":false,"given":"Ramon E.","family":"Diaz-Ramos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neil","family":"Hernandez-Gress","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose Luis","family":"Preciado","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hector G.","family":"Ceballos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,24]]},"reference":[{"key":"559_CR1","volume-title":"Measuring outcomes of college. 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