Crime Prediction Model using Three Classification Techniques: Random Forest, Logistic Regression, and LightGBM

Predicting the likelihood of a crime occurring is difficult, but machine learning can be used to develop models that can do so. Random forest, logistic regression, and LightGBM are three well-known classification methods that can be applied to crime prediction. Random forest is an ensemble learning algorithm that predicts by combining multiple decision trees. It is an effective method for classification tasks, and it is frequently employed for crime prediction because it handles imbalanced datasets well. Logistic regression is a linear model that can be used to predict the probability of a binary outcome, such as the occurrence of a crime. It is a relatively straightforward technique that can be effective for crime prediction if the features are carefully chosen. LightGBM is a gradient-boosting decision tree algorithm with a reputation for speed and precision. It is a relatively new algorithm, but because it can achieve high accuracy on even small datasets, it has rapidly gained popularity for crime prediction. The experimental results show that the LightGBM performs best for binary classification, followed by Random Forest and Logistic Regression.

@article{Alsubayhin2024,
  title     = {Crime Prediction Model using Three Classification Techniques: Random Forest, Logistic Regression, and LightGBM},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Abdulrahman Alsubayhin and Muhammad Sher Ramzan and Bander Alzahrani},
  doi       = {10.14569/IJACSA.2024.0150123},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150123}
}