Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Risk factor classification for drivers in Mexico using data science(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-05-31) Cadena Rodríguez, Rodrigo; Hernández Gress, Neil; puemcuervo,emipsanchez; Hernández Gress, Eva Selene; Ortiz Bayliss, José Carlos; Lozano Medina, Luis Angel; Escuela de Ingeniería y Ciencias; Campus Monterrey; Hervert Escobar, LauraThe aim of this dissertation is to find an optimal way to profile drivers in Mexico analysing different databases of car accidents and auto insurance claims inside this country and using gradient boosting algorithms. According to the National Public Health Institute, Mexico is in seventh-place globally and third place in Latin America in the most deaths caused by car accidents' ranking. Moreover, even when it is mandatory to have car insurance when having a car, only 30\% of people hires a car insurance. This is mainly because of the prices that insurance companies offer, and this happens because most of them are using old methods that do not consider all the crucial variables and treat all their customers as if everybody had the same risk for making a claim, even when companies in other countries are using some machine learning models that have been proved to be efficient and permitted a low-cost premium based on users profile.
- Analyzing factors that impact alumni income with a machine learning approach(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-05) Gómez Cravioto, Daniela Alejandra; Hernández Gress, Neil; puelquio; Ceballos Cancino, Héctor; López Guajardo, Rafael; Ceballos Cancino, Héctor Gibrán; School of Engineering and Sciences; Campus Monterrey; Preciado Arreola, José LuisThis thesis presents an exploration of different machine-learning algorithms and different approaches for predicting alumni income. The aim is to obtain insights regarding the strongest predictors for income and a ``high" earners class. The study examines the alumni sample data obtained from a survey from Tec de Monterrey, a multi-campus Mexican private university. Survey results encompass 17,898 observations before cleaning and preprocessing and 12,275 observations after this. 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. For the problem of income prediction, there have been several attempts in both social science and econometric studies. However, this study investigates whether the accuracy of conventional algorithms in econometric research to predict income can be improved with a data science approach. Furthermore, we present insights obtained with explainable AI techniques. The results show that the Gradient Boosting Model outperformed the parametric models, Linear Regression and Logistic Regression, in predicting the current income of alumni with statistically significant results (p<0.05) in three different approaches: OLS regression, Multi-class Classification, and Binary Classification. The study also identified that for predicting the alum's first income after graduation, the Linear and Logistic Regression models were the most accurate methods, as the non-parametric models did not show a significant improvement. Succinctly, we identified that age, gender, working hours per week, their first income after graduation, and those factors related to their job position and their firm contributed to explaining their income. Simultaneously, post-graduation education and family background had an insignificant contribution to the model. In addition, the results, which showed a gender wage gap indicate that further work is required to enable equality in Mexico.
- Financial Habits of Mexican Women using Machine Learning Algorithms(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-04) Lozano Medina, Jessica Ivonne; Hernández Gress, Neil; RR/tolmquevedo; Ceballos Cancino, Héctor Gibrán; Flores Segovia, Miguel Alejandro; School of Engineering and Sciences; Campus Monterrey; Hervert Escobar, LauraThis research was conducted under the Master in Computational Science program at Tecnológico de Monterrey. The proposal is a model to assess a profile risk for Mexican women, who require the service of a financial portfolio offered by a financial institution. Typically, women are scored with a lower financial risk than men. However, the understanding of variables and indicators that lead to such results, are not fully understood. Furthermore, the stochastic nature of the data makes it difficult to generate a suitable profile to offer an adequate financial portfolio to the women segment. Therefore, there is a great interest for developing methods that correctly model the behavior, and aid the decision-making process in financial services. Several models in the State-of-art for this type of analysis is done with linear programming and statistical techniques. Therefore, this study will use a benchmark of Machine Learning algorithms, such as Unsupervised and Supervised Learning algorithms, to extract information on four different datasets relevant to the population of interest. The first phase involves applying state-of-the-art techniques on public datasets of the Mexican population, whereas the second phase involves a future research involving a financial institution to create the model for the Women segment. It was found that financial habits of the population are heavily dependent on the region. There also an important group in the population characterized for not possessing an account in a financial institution and also not having emergency funds. In the case of the profiles of women, the most important attributes were their civil status and their participation in the workforce. The largest group of women are housewives, though the second largest group consists of married women who also participate in the workforce.