Prediction Analysis Of Criminal Data Using Machine Learning (original) (raw)

CRIME ANALYSIS AND PREDICTION USING MACHINE LEARNING

Crime is one of our society's most serious and pervasive problems, and preventing it is a critical duty. This necessitates keeping note of all offences and creating a database for future reference. The present issue is keeping a reliable crime record and analysing this data to aid in the prediction and resolution of future crimes. The objective of this paper is to analyze dataset, which consist of numerous crimes and predicting the type of crime, which may happen in future depending upon various conditions. In this project, we will be using the technique of machine learning and data science for crime prediction of Indian crime data set. Crime analysis and prediction is a methodical way to spotting crime. This algorithm can anticipate and depict crime-prone areas. Using the notion of machine learning, we may extract previously unknown, meaningful information from unstructured data. The extraction of new information is anticipated using current datasets. Crime is a perilous and widespread societal issue that affects people all around the world. Crime has an impact on people's quality of life, economic prosperity, and the nation's reputation. To safeguard their communities from crime, modern technology and novel techniques to enhancing crime analytics are required. We present a system that can analyse, identify, and forecast various crime probabilities in a given location. This study describes many sorts of criminal analysis and crime prediction using machine learning approaches.

Crime Data Analysis and Prediction

Crimes against women have created a negative impact on society both socially and economically. The Law is facing numerous challenges while trying to prevent crimes against women. Crimes against women are rapidly growing in India and it is a big threat to humanity. In every part of India, women face various issues such as kidnapping, domestic violence, acid attacks, rapes, etc. The crime data analytic system will be able to analyze and predict the rate of crimes happening in various states in India by using machine learning algorithms such as linear regression and random forest classifiers. By using this web application, the user will know the statistics and occurrence of crimes in one particular state with the data visualization done on the dataset available.

IRJET- PREDICTION of CRIME RATE ANALYSIS using MACHINE LEARNING APPROACH

IRJET, 2020

In recent years, report points out that the crimes in India have seen a spike. The report adds that the cases of murder, rapes, and kidnapping have seen a rise. Most of countries in the world have seen a remarkable increase in the crime rate. There is no particular reason for any trouble for criminal activities. To prevent this problem in police sectors have to predict crime rate using machine learning techniques. The aim is to investigate machine learning based techniques for crime rate by prediction results in best accuracy and explore in this work the applicability of data technique in the efforts of crime prediction with particular importance to the data set. The analysis of dataset by supervised machine learning technique(SMLT) to capture several information's like, variable identification, uni-variate analysis, bi-variate and multi-variate analysis, missing value treatments and analyze the data validation, data cleaning/preparing and data visualization will be done on the entire given dataset. Our analysis provides a comprehensive guide to sensitivity analysis of model parameters with regard to performance in prediction of crime rate by accuracy calculation from comparing supervise classification machine learning algorithms.

Machine Learning Analysis on Crime Prediction System

In the existing day situation, the utilized sciences like Data mining and machine learning, gaining expertise of have supply up necessary elements of crime detection and prevention for analysis. Crime in current day society having a massive, troubling hassle that is prevailing which makes it challenging to preserve away from in the civilization. Most of the situations are being recorded quotidian basis at most places. Since a range of instances have been registered, it is wished to hold a database for future purposes. The contemporary problem that is confronted is maintaining by way of way of allowable crime dataset and inspecting the records and maintain shut the troubles that would maybe additionally be helpful for future use to seize the previous and present-day crimes. We use computer analyzing algorithms for inspecting and predicting criminal things to do from the crime dataset. Websites like Kaggle affords required datasets. Data is a mixture of a kind of crime, description, time and date, latitude, and longitude. After gathering datasets pre-processing is carried out to take away noisy facts and fill incomplete archives which leads to excessive accuracy. Algorithm that we use are Light GBM and Random wooded region will be carried out for crime estimation, predictable and totally the algorithm which furnish excessive accuracy is be chosen for evaluation the result.

Crime Prediction and Analysis Using Machine Learning

IJCSMC, 2022

It is critical to recognise crime patterns in order to be better prepared to respond to criminal behavior. We study crime data of states in india that was scrapped publicly available websites kaggle for our project. The goal is to estimate which type of crime is most likely to occur at a given time and location. The use of AI and machine learning to identify crime using sound or video is now in use, has been demonstrated to function, and is likely to grow. The use of AI/ML to forecast crimes or a person’s chance of committing a crime has potential, but it is still a work in progress. The most difficult task will most likely be “proving” to legislators that it works. It’s tough to establish the negative when a system is meant to prevent something from happening. A positive feedback loop would certainly benefit companies who are directly involved in providing governments with AI capabilities to monitor areas or predict crime. Improvements in crime prevention technology will almost certainly lead to an increase in overall spending on this technology. We also try to make our categorization work more relevant by grouping many classes together into larger groups. Finally, we present and discuss our findings using several classifiers, as well as future research directions.

An Application for Risk of Crime Prediction Using Machine Learning

2021

The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Mac...

Survey on Crime Interpretation and Forecasting Using Machine Learning

IRJET, 2022

Understanding the crime pattern and taking safety measures to solve the crime problems has become the important factor in today's world. The main aim here is to locate the crime location and find the pattern based on the time and place in Bangalore city. There is a lot of rise in the software systems which helps the officers to solve the crime issues. We will be looking at the various machine learning algorithms which helps to detect the crime pattern and helps in solving the crime in very less time. There are different algorithms in machine learning like k-means clustering algorithm which can be used in the process of identification of crime. The Data mining approach is used to predict the features of crime dataset that affects the high crime rate. There are two types of algorithms i.e., supervised and unsupervised, supervised algorithms that is used to evaluate the training dataset for its accuracy and unsupervised algorithms helps in solving the unlabelled data into classes or clusters. There are other few different learning methods like random forest in data mining based on unknown and the previous year collected datasets which can be used for predicting the crime pattern based on the time and place.

Assist Crime Prevention Using Machine Learning

SSRN Electronic Journal, 2019

Crime rate is increasing significantly over the years. Crime prevention is the attempt to reduce and deter the crimes and the criminals. The government must go beyond law enforcement and criminal justice to tackle the risk factors that cause crime because it is more cost effective and leads to greater social benefits. The data driven method is used which is based on the broken windows theory, having an enormous impact on the working of the police department. The theory links disorder and incivility within a community to subsequent occurrences of serious crimes. Predictive policing is used by the law enforcement stakeholders for taking proactive measures against crimes. This will help the police departments to efficiently focus their resources on the potential crime hotspots. The model is built to predict the crime rate based on demographic and economic information of particular localities using decision trees, linear classification, regression and spatial analysis.

SURVEY ON CRIME ANALYSIS AND PREDICTION USING MACHINE LEARNING TECHNIQUES

International Journal of Trendy Research in Engineering and Technology, 2022

As of late, wrongdoing has turned into a noticeable type of mischief to individuals and society. The ascent in wrongdoing is making imbalance in the society. To examine and answer to the idea of a wrongdoing, it is critical to see how the wrongdoing was perpetrated. It is a wrongdoing used to recognize wrongdoing in each side of the globe. The principal aim of this venture is to decrease wrongdoing in the most impacted regions. In this task, we anticipate the assortment of wrongdoings, the most apparent month, the most noticeable time, and the date of event. Some AI calculations, like Naive Bayes, were portrayed in this work to carry out various sorts of wrongdoing, and the current uprightness was higher than arranged.

USING MACHINE LEARNING ALGORITHMS TO ANALYZE CRIME DATA

Data mining and machine learning have become a vital part of crime detection and prevention. In this research, we use WEKA, an open source data mining software, to conduct a comparative study between the violent crime patterns from the Communities and Crime Unnormalized Dataset provided by the University of California-Irvine repository and actual crime statistical data for the state of Mississippi that has been provided by neighborhoodscout.com. We implemented the Linear Regression, Additive Regression, and Decision Stump algorithms using the same finite set of features, on the Communities and Crime Dataset. Overall, the linear regression algorithm performed the best among the three selected algorithms. The scope of this project is to prove how effective and accurate the machine learning algorithms used in data mining analysis can be at predicting violent crime patterns.