IDENTIFICATION OF OFFENCE HOTSPOT USING RANDOM FOREST ALGORITHM

Abstract

This study aims to identify and predict criminal 
hotspots using machine learning techniques. Crime is 
one of the most pressing societal issues, and 
preventing it is critical. This requires tracking and 
maintaining records of all crimes for future reference. 
The proposed system utilizes the Random Forest 
algorithm, a popular and effective supervised machine 
learning technique, to detect and predict crime 
hotspots. Random Forest can perform both 
classification and regression tasks, and it addresses the 
tendency of decision trees to overfit the training data. 
The algorithm works by creating multiple decision 
trees and determining the output based on the mean or 
mode of the individual tree predictions. In addition to 
the Random Forest model, the study also employs data 
visualization techniques, such as bar charts, line 
charts, and heatmaps, to analyze the crime dataset and 
discover patterns and trends. These visualizations aid 
in identifying high-crime areas, historical trends, and 
potential factors influencing criminal behavior. The 
proposed system was evaluated using a publicly 
available crime dataset, and the results demonstrate the 
effectiveness of the Random Forest algorithm in 
predicting crime hotspots. The integration of machine 
learning algorithms, such as Random Forest, and data 
visualization techniques can provide valuable insights 
into crime data, enabling law enforcement agencies to 
make informed decisions and develop efficient crime 
prevention strategies.