The objective of this project is to tackle a vital issue in the society – Crimes. Analyzing and examining of crimes happening in the world will give us a Broadview in understanding the crime regions and can be used to take necessary precautions to mitigate the crime rates. Identifying Crime patterns will allow us to tackle problems with unique approaches in specific crime category regions and improve more security measures in society. Current studies show the reason of increase in crime rates is more in areas that are economically backward. In few decades’ property crime will be a target. The following approach involves predicting crimes classifying, pattern detection and visualization with effective tools and technologies. Use of past crime data trends helps us to correlate factors which might help understanding the future scope of crimes.
San Francisco was infamous for housing some of the world’s most notorious criminals on the inescapable island of Alcatraz. Today, the city is known more for its tech scene than its criminal past. From Sunset to SOMA, and Marina to Excelsior, this project analysis 12 years of crime reports from across all of San Francisco’s neighbourhoods to create a model that predicts the category of crime that occurred, given time and location.
In this system they are proposed logistic regression and Knn classifiers. Researches have been done to study the relation between criminal activities and socio-economic variables like unemployment income level race level of education
- It supports only for small and medium level data’s only.
- It gives the less accuracy
- In existing system, they are use traditional ML algorithm like logistic regression
Crime is a social phenomenon as old as societies themselves, and although there will never be a free from crime society – just because it would need everyone in that society to think and act in the same way – societies always look for a way to minimize it and prevent it. In the modern United States history, crime rates increased after World War II, peaking from the 1970s to the early 1990s. Violent crime nearly quadrupled between 1960 and its peak in 1991. Property crime more than doubled over the same period. Since the 1990s, however, crime in the United States has declined steadily. Until recently crime prevention was studied based on strict behavioural and social methods, but the recent developments in Data Analysis have allowed a more quantitative approach in the subject. We will explore a dataset of nearly 12 years of crime reports from across all of San Francisco’s neighbourhoods, and we will create a model that predicts the category of crime that occurred, given the time and location.
- it should give best accuracy value
- It accepts large level data’s
- we are using modern algorithm like K-means
Hardware and Software Requirements:
- Windows 7,8,10 64 bit
- RAM 4GB
- Python 2.7
- Anaconda Navigator
we believe that crime data mining has a promising future for increasin the effectiveness and efficiency of criminal and intelligence analysis. Visual and intuitive criminal and intelligence investigation techniques developed for crime pattern. As we have applied clustering technique of data mining for crime analysis, we can also perform other techniques of data mining such as classification. Also, we performed analysis on various dataset.
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 A.Malathi ,Dr.S.Santhosh Baboo. D.G. Vaishnav College,Chennai ,2011 Algorithmic Crime Prediction Model Based on the Analysis of Crime Clusters.
 Malathi.A 1 ,Dr.S.Santhosh Baboo 2 and Anbarasi . A 31 Assistant professor ,Department of Computer Science ,Govt Arts College ,Coimbatore , India . 2 Readers , Department of Computer science , D.G. Vaishnav Collge ,Chennai , India , 2011 An intelligent Analysis of a city Crime Data Using Data Mining
 Malathi , A; Santhosh Baboo , S, 2011 An Enhanced Algorithm to Predict a Future Crime using Data Mining
 Kadhim B.Swadi al-Janabi . Department of Computer Science . Faculty of Mathematics and Computer Science .University of Kufa/Iraq , 2011 A Proposed Framework for Analyzing Crime DataSet using Decision Tree and Simple K-means Mining Algorithms.
 Aravindan Mahendiran, Michael Shuffett, Sathappan Muthiah, Rimy Malla, Gaoqiang Zhang,2011 Forecasting Crime Incidents using Cluster Analysis and Bayesian Belief Networks