TY - JOUR
T1 - Comparing regression models to predict property crime in high-risk Lima districts
AU - Escobedo, Maria
AU - Tapia, Cynthia
AU - Gutierrez, Juan
AU - Ayma, Victor
N1 - Publisher Copyright:
© (2024), (Science and Information Organization). All Rights Reserved.
PY - 2024
Y1 - 2024
N2 - Crime continues to be an issue, in Metropolitan Lima, Peru affecting society. Our focus is on property crimes. We recognized the lack of studies on predicting these crimes. To tackle this problem, we used regression techniques such as XGBoost, Extra Tree, Support Vector, Bagging, Random Forest and AdaBoost. Through GridsearchCV we optimized hyperparameters to enhance our research findings. The results showed that Extra Tree Regression stood out as the model with an R2 value of 0.79. Additionally, error metrics like MSE (185.43) RMSE (13.62) and MAE (10.47) were considered to evaluate the model's performance. Our approach considers time patterns in crime incidents. Contributes, to addressing the issue of insecurity in a meaningful way.
AB - Crime continues to be an issue, in Metropolitan Lima, Peru affecting society. Our focus is on property crimes. We recognized the lack of studies on predicting these crimes. To tackle this problem, we used regression techniques such as XGBoost, Extra Tree, Support Vector, Bagging, Random Forest and AdaBoost. Through GridsearchCV we optimized hyperparameters to enhance our research findings. The results showed that Extra Tree Regression stood out as the model with an R2 value of 0.79. Additionally, error metrics like MSE (185.43) RMSE (13.62) and MAE (10.47) were considered to evaluate the model's performance. Our approach considers time patterns in crime incidents. Contributes, to addressing the issue of insecurity in a meaningful way.
KW - crime
KW - machine learning
KW - prediction
KW - regression
KW - Supervised techniques
KW - Técnicas supervisadas
KW - Aprendizaje automático
KW - Regresión
KW - Crimen
KW - Predicción
UR - http://www.scopus.com/inward/record.url?scp=85189935904&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/88eb8922-7eef-3587-9df7-394a9b6fd5c3/
UR - https://hdl.handle.net/20.500.14139/EC9JNP
U2 - 10.14569/IJACSA.2024.0150307
DO - 10.14569/IJACSA.2024.0150307
M3 - Article in a journal
AN - SCOPUS:85189935904
SN - 2158-107X
VL - 15
SP - 62
EP - 68
JO - International Journal of Advanced Computer Science and Applications
JF - International Journal of Advanced Computer Science and Applications
IS - 3
ER -
