NIRBHAY: Analysis and Prediction of Crime
Authors:-Ms. Sayali Angat, Mr.Kapil Gharat, Mr.Angarak Gurav, Ms. Harshala Patil, Professor Prachi Sorte
Abstract-The increasing complexity and scale of urban areas requires new crime analysis and prediction strategies The research project focuses on harnessing the power of machine learning algorithms to analyze historical crime data and predict future crime in specific areas. This study explores the potential of predictive analytics for crime prevention and law enforcement using clinical data that includes various crime types, demographic data, and characteristics of the area. This approach involves pre-processing the dataset to resolve missing values, outliers, and feature engineering to extract relevant information. To find patterns and correlations between variables, a range of learning models such as support vector machines, random forests, and decision trees are fed into the data. Utilize measures like accuracy, precision, recall, and F1 score to assess the performance of the model. Furthermore, the actual offense is incorporating predictive models into internet platforms in order to allow users to get predictions based on characteristics like time, location, and publicly accessible data. The platform provides law enforcement, legislators and urban planners with information to better allocate resources and prevent crime. The results demonstrate the effectiveness of the machine learning process in crime analysis and prediction, with high accuracy in predicting crime scenes. The use of the website promotes easy access and usability, providing participants with the skills to reduce crime and increase public safety in the city.
