Crime Analytics using Machine Learning

Prof. Suman Acharya
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引用次数: 0

Abstract

Crime is one of the most significant and pervasive problems in our society, and preventing it is a crucial duty. A large number of crimes are perpetrated each day. Maintaining and analyzing crime data to forecast and solve crimes is the current issue. This project analyzes a large dataset of crimes and predicts future crimes based on conditions. This project uses data science and machine learning for India's crime data prediction. Thus, Decision Tree, Logistic Regression, Multi-Regression, k-NN, Lasso & Ridge, and Random Forest are all involved in the supervised classification problem. Predicting crimes and classifying effective pattern detection and visualization equipment Utilizing crime data trends from the past allows us to correlate aspects that may help us comprehend the breadth of crimes in the future. This study uses visualization and machine learning methods to estimate future crime rates. First, raw datasets were processed and displayed.
使用机器学习的犯罪分析
犯罪是我们社会中最严重和最普遍的问题之一,预防犯罪是一项至关重要的责任。每天都有大量的犯罪发生。维护和分析犯罪数据以预测和解决犯罪是当前的问题。该项目分析了大量的犯罪数据集,并根据情况预测未来的犯罪。这个项目使用数据科学和机器学习来预测印度的犯罪数据。因此,决策树、逻辑回归、多元回归、k-NN、Lasso & Ridge和随机森林都涉及到监督分类问题。预测犯罪和分类有效的模式检测和可视化设备利用过去的犯罪数据趋势使我们能够将各个方面联系起来,从而帮助我们了解未来犯罪的广度。这项研究使用可视化和机器学习方法来估计未来的犯罪率。首先,对原始数据集进行处理和显示。
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