Our goal in this project is to create a thorough crime prediction model for the South Australia region in Australia, with an emphasis on including deterrent practices to anticipate crime rates. We identified intricate links and patterns in the crime data by utilizing the Random Forest algorithm, a cutting-edge AI method.Our main goal is to develop a contextspecific, highly precise crime prediction model that takes into consideration South Australia particular peculiarities.
This project explored the application of machine learning techniques for crime prediction using historical crime data. The research makes use of a number of machine learning algorithms, such as K-Nearest Neighbours (KNN), ridge regression, Linear Support Vector Classification (SVC), and Gaussian Naive Bayes, to help anticipate criminal activity in the South Australian region. Through comparative analysis, the study aims to identify the most effective model among these approaches.The outcomes of this project facilitated efficient resource allocation, allowing efforts to be focused on areas with a higher likelihood of criminal activities.
The project aims to develop a precise crime prediction model using advanced machine learning methods like Random Forest, Gaussian Naive Bayes, Ridge Regression, Linear Support Vector Classification, and K-Nearest Neighbours. The model will use historical data and anti-crime features to forecast crime rates. The research will provide evidence-based insights for law enforcement, policymakers, and community stakeholders, facilitating targeted resource allocation and crime prevention strategies. Crime prediction is crucial for law enforcement and public safety. Recent advancements in AI offer new opportunities for reliable crime prediction models. This project aims to create a comprehensive crime prediction model for South Australia, focusing on deterrent practices. Using the Random Forest algorithm, the model identifies intricate patterns in crime data. It offers practical insights for crime prevention and promotes evidence-based decision-making by incorporating anti-crime characteristics like community policing programs and law enforcement tactics. The South Australian crime dataset is the foundation of our analysis, which was meticulously prepared through a thorough data cleaning process, encoding categorical variables, and structuring dates. Data aggregation at various levels improved the predictive models’ adaptability and accuracy. The project involved regression and classification tasks using different machine learning techniques, such as :
- Principal Component Analysis (PCA)
- Ridge Regression
- K-Nearest Neighbours Regression
- Classification Random Forest Classifier
- Support Vector Classifier
- Gaussian Naive Bayes
- Regression (R-Squared) - 0.99
- Random Forest Accuracy - 0.98
- Support Vector - 0.96
- Gaussian Naive Bayes - 0.97