Crime Prediction Using Ensemble Approach

@article{1706531,
      author = {Arjun K, Suchetha N V, Panchami B S},
      title = {Crime Prediction Using Ensemble Approach},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {5},
      pages = {385-391},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706531.pdf},
      abstract = {In today's context, preventing crime is increasingly vital to safeguard communities and ensure public safety. Similar to how vaccinations shield children from diseases, a proactive approach to crime prevention aims to mitigate risks before they escalate. This involves not only educating the public and enhancing awareness but also implementing efficient policing strategies and employing technology-driven predictive models. By analysing historical crime data alongside geographic and demographic factors, this study employs advanced analytical techniques. These methods aim to uncover correlations between socio-economic conditions, environmental factors, and criminal activities. By integrating machine learning algorithms and statistical models, the research strives to enhance the accuracy of crime prediction. Ultimately, the findings seek to empower law enforcement agencies with actionable insights. This enables them to adopt pre-emptive measures, fostering a safer environment and bolstering community resilience against crime.},
      keywords = {Crime prevention, Crime prediction models, Machine learning techniques, Socioeconomic factors, Geospatial analysis, Demographic factors, Predictive analytics, Statistical modelling, Risk assessment, Crime prevention strategies},
      month = {November},
  }