Evaluating Crime Type And Crime Pattern Analysis Using Machine Learning

The judicial system's success relies on minimizing crime and disciplining those involved in crimes. The crime rate has increased, making it a laborious and time-consuming task for law enforcement. The paper proposes leveraging business analytics and machine learning algorithms to speed up the process and understand the logic of crimes. The main issues in primary investigations are addressed by a self-sustaining model trained on updated datasets. The model depends on the dataset, which is constantly replicating and constantly sourcing. However, the main issue with machine learning models is the fixed estimates of data. To address this, a model that can continuously train itself and provide accurate results with differentiable data is needed. Visual representation is also crucial in any case, and future enhancements will focus on improving visual models to find different possibilities. Data mining is a major area of interest in this paper, which involves collecting raw data and creating information for accurate predictions. Common tools used for clustering include neural networks and survival analysis. Forecasting aims to discover patterns and data that may lead to reasonable predictions and estimates future value based on a record's pattern. The project aims to provide an accurate solution to this problem.