International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022
p-ISSN: 2395-0072
www.irjet.net
Predictive Modeling for Topographical Analysis of Crime Rate Prof. Vrushali V. Kondhalkar1, Antara More2, Bhawana Singh3, Jaspreet Singh Rahi4, Sudipti Ranjan5 1Prof.
Vrushali V. Kondhalkar, Dept. Of Computer Engineering, Pune, Maharashtra, India 2Antara More, Dept. Of Computer Engineering, Pune, Maharashtra, India 3Bhawana Singh, Dept. Of Computer Engineering, Pune, Maharashtra, India 4Jaspreet Singh Rahi, Dept. Of Computer Engineering, Pune, Maharashtra, India 5Sudipti Ranjan, Dept. Of Computer Engineering, Pune, Maharashtra, India ---------------------------------------------------------------***----------------------------------------------------------------Abstract - Criminal activities are increasing all over the world. It is important to reduce crime as it directly affects the
country's economic growth. Therefore, there is an urgent need for security agencies to fight and reduce crime in the community. The proposed system helps us to detect crime and resolve criminal cases quickly based on data collected using machine learning strategies. The system helps to predict the type of crime in a particular area based on crime patterns. In this project, we will be using a machine learning method. Contains important information about crime reporting such as date, type of crime, location of the crime, etc. The data is downloaded from a database called kaggle.com and is pre-processed so that we can extract the most important natural features of crime reporting such as roads or a few places, dates, and times, and areas with a higher crime rate than others. This data is used as an incentive to predict and resolve crime at an instant rate. This project will help us to find a way to improve the crime detection system, the type of crime that will occur in a particular area, and the way to improve investigative efforts of any kind of crime. Keywords: Machine learning, Crime prediction
1. INTRODUCTION Today crime is on the rise worldwide. It affects the quality of life and the development of economic well-being and the dignity of the nation. It directly affects the nation's financial growth by burdening the government with the financial burden due to the need for more police, and criminal justice courts. In terms of public safety, there is a need for more sophisticated ways to improve crime analysis to protect their communities. Accurate forecasts of crime help to reduce crime but remain problematic as crime relies on many complex issues. The basic pattern of crime and its relationship to the region or region helps us to identify and predict crime in a particular area. According to a previous study, it is clear that in every city there are fewer roads or areas with a higher crime rate than others. Crime can be predicted as criminals become more active and active in their comfort zone. When they succeed they try to repeat the crime in the same place. The occurrence of a crime depends on several factors such as criminal intelligence, local security, etc. Usually, Criminals choose the same location and time to try the next crime. While it may not be true in all cases, the chances of recurrence are high, as per the study, and this makes crime predictable. Predicting crime patterns is an important function in developing more effective crime prevention strategies or developing investigative efforts based on the availability of prior data such as case information, location, date, and time. Here, we use machine learning techniques to predict crime and its types in crime hotspots. Machine Learning is a form of practical intelligence that helps us to identify patterns using data analysis. There are three stages: 1) The dataset is extracted from the official site. 2) With the help of a machine learning algorithm, using python as core we can predict the type of crime that will occur in a particular area. 3) The model would be trained for prediction. The training would be done using the training data set which will be validated using the test dataset uploaded using the Kaggle website.
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