Análisis de las causas de deserción de los estudiantes de la facultad de ingenierías del instituto tecnológico metropolitano (ITM)

The fundamental purpose of this project is to develop a model to analyze and understand the reasons behind academic dropout rates at the ITM School of Engineering. To achieve this, dashboards will be implemented to simplify decision-making at the institutional level. The process is structured in several phases, beginning with an exploratory analysis where statistically significant variables are identified. The dashboard is then constructed, where the results are presented in a precise and understandable manner using graphs, tables, and visualizations. The results of the exploratory analysis reveal variables such as age, stratum, level, seniority, PP, and origin, which are correlated with academic status. These findings are fundamental to understanding behavioral patterns and factors influencing student dropout rates. The next step involves implementing a machine learning model, where six key variables are used for its design and training. Seniority, age, and level are identified as crucial variables in predicting academic status. Model performance is anticipated to improve with the addition of more data, and exploration of new variables to enhance predictions is suggested. In conclusion, this project offers a comprehensive approach that combines statistical analysis and machine learning models to understand and prevent academic dropout.