Crime rate analysis using k nn in python
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The document presents a crime rate analysis system using the k-NN algorithm to predict and reduce crime by analyzing historical data, identifying patterns, and clustering crime incidents. It highlights the advantages of the proposed system over existing methods, including user-friendliness and efficiency in crime prediction and prevention. Additionally, it outlines the hardware and software requirements for implementing the system, as well as some limitations.
![Crime rate analysis using K-NN in python
Abstract:
Crime rate is increasing now-a-days in many countries. In today's world with such higher
crime rate and brutal crime happening, there must be some protection against this crime. Here we
introduced a system by which crime rate can be reduced. Crime data must be fed into the system.
We introduced data mining algorithm to predict crime. KNN algorithm plays an important role in
analyzing and predicting crimes. KNN algorithm will cluster co-offenders, collaboration and
dissolution of organized crime groups, identifying various relevant crime patterns, hidden links,
link prediction and statistical analysis of crime data. This system will prevent crime occurring in
society. Crime data is analyzed which is stored in the database. Data mining algorithm will
extract information and patterns from database.
System will group crime. Clustering will be done based on places where crime occurred,
gang who involved in crime and the timing crime took place. This will help to predict crime
which will occur in future. Admin will enter crime details into the system which is required for
prediction. Admin can view criminal historical data. Crime incident prediction depends mainly
on the historical crime record and various geospatial and demographic information.
Existing System:
Data mining in the study and analysis of criminology can be categorized into main
areas, crime control and crime suppression. De Bruin et. al. [1] introduced a framework for crime
trends using a new distance measure for comparing all individuals based on their profiles and
then clustering them accordingly. Manish Gupta et. al. [2]. highlights the existing systems used
by Indian police as e-governance initiatives and also proposes an interactive query based
interface as crime analysis tool to assist police in their activities. He proposed interface which is
used to extract useful information from the vast crime database maintained by National Crime
Record Bureau (NCRB) and find crime hot spots using crime data mining techniques such as
clustering etc. The effectiveness of the proposed interface has been illustrated on Indian crime
records.
Disadvantages:
Users who don't have internet connection can't access the system.
Admin must enter correct records otherwise system will provide wrong information
ProposedSystem:
The procedure is given below:
1. First we take crime dataset
2. Filter dataset according to requirement and create new dataset which has attribute according
to analysis to be done](https://image.slidesharecdn.com/crimerateanalysisusingk-nninpython-181111060319/85/Crime-rate-analysis-using-k-nn-in-python-1-320.jpg)
Crime rate analysis using k nn in python
- 1. Crime rate analysis using K-NN in python Abstract: Crime rate is increasing now-a-days in many countries. In today's world with such higher crime rate and brutal crime happening, there must be some protection against this crime. Here we introduced a system by which crime rate can be reduced. Crime data must be fed into the system. We introduced data mining algorithm to predict crime. KNN algorithm plays an important role in analyzing and predicting crimes. KNN algorithm will cluster co-offenders, collaboration and dissolution of organized crime groups, identifying various relevant crime patterns, hidden links, link prediction and statistical analysis of crime data. This system will prevent crime occurring in society. Crime data is analyzed which is stored in the database. Data mining algorithm will extract information and patterns from database. System will group crime. Clustering will be done based on places where crime occurred, gang who involved in crime and the timing crime took place. This will help to predict crime which will occur in future. Admin will enter crime details into the system which is required for prediction. Admin can view criminal historical data. Crime incident prediction depends mainly on the historical crime record and various geospatial and demographic information. Existing System: Data mining in the study and analysis of criminology can be categorized into main areas, crime control and crime suppression. De Bruin et. al. [1] introduced a framework for crime trends using a new distance measure for comparing all individuals based on their profiles and then clustering them accordingly. Manish Gupta et. al. [2]. highlights the existing systems used by Indian police as e-governance initiatives and also proposes an interactive query based interface as crime analysis tool to assist police in their activities. He proposed interface which is used to extract useful information from the vast crime database maintained by National Crime Record Bureau (NCRB) and find crime hot spots using crime data mining techniques such as clustering etc. The effectiveness of the proposed interface has been illustrated on Indian crime records. Disadvantages: Users who don't have internet connection can't access the system. Admin must enter correct records otherwise system will provide wrong information ProposedSystem: The procedure is given below: 1. First we take crime dataset 2. Filter dataset according to requirement and create new dataset which has attribute according to analysis to be done
- 2. 3. Open rapid miner tool and read excel file of crime dataset and apply "Replace Missing value operator" on it and execute operation 4. Perform "Normalize operator" on resultant dataset and execute operation 5. Perform k means clustering on resultant dataset formed after normalization and execute operation 6. From plot view of result plot data between crimes and get required cluster 7. Analysis can be done on cluster formed Advantages: Helps to prevent crime in society System will keep historical record of crime. System is user friendly Saves time SYSTEM CONFIGURATION: Hardware requirements: Processer : Any Update Processer Ram : Min 1 GB Hard Disk : Min 100 GB Software requirements: Operating System : Windows family Technology : Python 3.6 IDE : PyCham UML : Star UML DFD : DFD Drawer Implemented by Development team : Cloud Technologies Website : http://www.cloudstechnologies.in Contact : 8121953811, 040-65511811