comparative study of different prediction algorithm for crime analysis

comparative study of different prediction algorithm for crime analysis
Author(s):
saksham goyal , nikhil pratap singh, aman arora , pulkit goel , Bindu Garg
Keywords:
Crime analysis, Crime prediction, Decision tree, Linear classification, Regression, Clustering
Abstract
In this modern era, crime is still one of the most prevalent problems in our country. It can be easily said that crime rate has not come down even after 75 years of independence. But, recently lots of efforts are been taken to reduce the crime rate using latest technologies like machine learning, data analysis etc. Crime analysis is defined as a set of systematic analytics processes providing timely and useful information on crime patterns and trends. Data mining is the procedure which include evaluating large pre-existing databases in order to generate new information. The extraction of new information is predicted using existing datasets. In recent years, crime data from different heterogeneous sources have given immense opportunities to the research community to effectively study crime prediction tasks in actual real data. In this research paper, we are going to study different prediction algorithms for crime analysis and implement them on our dataset. We will also compare these algorithms to find out the most suitable one.
Article Details
Unique Paper ID: 155970 Publication Volume & Issue: Volume 9, Issue 2 Page(s): 449 - 457
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