IEEE BASE PAPER TITLE:
Crime Prediction Using Machine Learning and Deep Learning
OUR PROPOSED PROJECT TITLE:
Advanced Crime Prediction Using Machine Learning with Crime Forecasting and Categorization
IEEE BASE PAPER ABSTRACT:
Predicting crime using machine learning and deep learning techniques has gained considerable attention from researchers in recent years, focusing on identifying patterns and trends in crime occurrences. This review paper examines over 150 articles to explore the various machine learning and deep learning algorithms applied to predict crime. The study provides access to the datasets used for crime prediction by researchers and analyzes prominent approaches applied in machine learning and deep learning algorithms to predict crime, offering insights into different trends and factors related to criminal activities. Additionally, the paper highlights potential gaps and future directions that can enhance the accuracy of crime prediction. Finally, the comprehensive overview of research discussed in this paper on crime prediction using machine learning and deep learning approaches serves as a valuable reference for researchers in this field. By gaining a deeper understanding of crime prediction techniques, law enforcement agencies can develop strategies to prevent and respond to criminal activities more effectively.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
Decision Tree Classifier and Bagging Classifier.
OUR PROPOSED PROJECT ABSTRACT:
“Crime Prediction Using Machine Learning” is a comprehensive project developed in Python that employs Machine Learning algorithms, specifically the Decision Tree Classifier and Bagging Classifier, to predict and classify various crime categories in Portland, Oregon, USA, from the years 2015 to 2023. The dataset utilized for this project consists of 505,063 data points, with a focus on 20 distinct crime classes, including ‘Larceny Offenses,’ ‘Motor Vehicle Theft,’ ‘Assault Offenses,’ ‘Drug/Narcotic Offenses,’ and others.
The Decision Tree Classifier yielded impressive results, achieving a 98% accuracy on the training set and a 95% accuracy on the test set. Similarly, the Bagging Classifier demonstrated robust performance, achieving a 98% accuracy on the training set and maintaining a 95% accuracy on the test set. These high accuracies indicate the effectiveness of the machine learning models in predicting and classifying crimes.
The dataset encompasses 15 features, including address, case number, crime against category (Person, Property, or Society), neighborhood, occur date, occur time, offense category, offense type, open data latitude/longitude, open data X/Y, and offense count. These features provide a comprehensive and diverse set of information, enabling the models to make accurate predictions.
The project’s significance lies in its potential application for law enforcement agencies, city planners, and policymakers. By accurately predicting and classifying crimes, it facilitates proactive decision-making and resource allocation, contributing to the enhancement of public safety and the efficient utilization of law enforcement resources.
The classification of crimes into specific categories allows for a more nuanced understanding of crime patterns, enabling stakeholders to implement targeted interventions and preventive measures. The high accuracy of the models demonstrates their reliability and effectiveness in handling real-world crime prediction scenarios.
In summary, “Crime Prediction Using Machine Learning” presents a robust and accurate approach to crime prediction, leveraging advanced algorithms and a rich dataset. The project’s success in accurately classifying diverse crime categories makes it a valuable tool for enhancing public safety and optimizing law enforcement strategies in urban environments.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED.
- Input Devices : Keyboard, Mouse.
- Ram : 8 GB.
- Operating system : Windows 10 Pro.
- Coding Language : Python 3.10.9.
- Web Framework : FLASK.
VARUN MANDALAPU, LAVANYA ELLURI, PIYUSH VYAS, AND NIRMALYA ROY, “Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions”, IEEE Access (Volume: 11), 2023.