Citas bibligráficas
Chavez, R., De, L., Lari, L. (2021). Denuncias de violencia familiar por agresión psicológica en el Perú [Trabajo de investigación, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/660175
Chavez, R., De, L., Lari, L. Denuncias de violencia familiar por agresión psicológica en el Perú [Trabajo de investigación]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2021. http://hdl.handle.net/10757/660175
@misc{renati/399640,
title = "Denuncias de violencia familiar por agresión psicológica en el Perú",
author = "Lari Castañon, Luciano Cesar",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2021"
}
This research work seeks the analysis of the current conditions in terms of violence in Peru; To do this, it relies on the Ministry of Women and Vulnerable Populations: an organization that is in charge of ensuring that the complaints of every citizen who is in Peruvian territory are attended to. The research problem was to observe the levels of complaint throughout Peru; According to statistics, it was found that violence classified as violence due to psychological aggression is the one that obtained a constant and ascending increase in the period 2016 - 2019. That is why the interest in identifying the factors that are related to the increase in complaints and as an objective The main thing is to create a model that can predict the level of psychological complaints at the national level. To achieve the objective of this research work, the data science methodology was used. Structured refined information was obtained from reliable sources and 29 related variables were analyzed with this. Throughout the work, a predictive model was executed using a supervised learning method and classification technique: KNN algorithm, obtaining a level of confidence in the prediction of the model of 75%. The foregoing contributed to detail the conclusions, which hope to help the organization obtain a prediction on the level of complaints caused by psychological violence; thereby favoring better decision-making at the operational level.
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