IRJET- Prediction of Crime Rate Analysis using Supervised Classification Machine Learning Approach

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 6771
Prediction of Crime Rate Analysis Using Supervised Classification
Machine Learning Approach
Kirthika V1, Krithika Padmanabhan A 2, Lavanya M3, Lalitha S D4
1Student, Computer Science and Engineering, RMK Engineering College, Tamil Nadu, India
2 Student, Computer Science and Engineering, RMK Engineering College, Tamil Nadu, India
3 Student, Computer Science and Engineering, RMK Engineering College, Tamil Nadu, India
4 Assistant Professor, Computer Science and Engineering, RMK Engineering College, Tamil Nadu, India
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Abstract - In recent years, report points out that the
crimes in India have seen a spike. There is no particular
reason for any trouble for criminal activities. Sometimes
society, cultural factors, different family systems, political
influences and law enforcement are responsible for the
criminal activities of an individual. Crime can be found in
various categories. To prevent this problem in police sectors,
we must predict crime rate using machine learning
techniques. The aim is to investigate machine learning
based techniques for crime rate by prediction results in best
accuracy and explore in this work the applicability of data
technique in the efforts of crime prediction with particular
importance to the data set. The analysis of dataset by
supervised machine learning technique (SMLT) to capture
several information’s like, variable identification, uni-
variate analysis, bi-variate and multi-variate analysis,
missing value treatments and analyse the data validation,
data cleaning/preparing and data visualization will be done
on the entire given dataset. Our analysis provides a
comprehensive guide to sensitivity analysis of model
parameters with regard to performance in prediction of
crime rate by accuracy calculation from comparing
supervise classification machine learning algorithms
Key Words: Dataset, Crime rate analysis, Machine
Learning-Classification method, Python, Prediction of
Accuracy result.
1.INTRODUCTION
1.1 Crimes are the significant threat to the humankind.
There are many crimes that happens regular interval
of time. Crimes have different types are robbery,
murder, rape, assault, battery, false imprisonment,
kidnapping, homicide. Since crimes are increasing
there is a need to solve the cases in a much faster way.
The crime activities have been increased at a faster
rate and it is the responsibility of police department
to control and reduce the crime activities.
1.2 Crime prediction and criminal identification are the
major problems to the police department as there are
tremendous amount of crime data that exist. There is
a need of technology through which the case solving
could be faster. The problem made me to go for a
research about how can solve a crime case made
easier. Through many documentation and cases, it
came out that machine learning and data science can
make the work easier and faster.
1.3 The aim of this project is to make crime prediction
using the features present in the dataset. The dataset
is extracted from the official sites. With the help of
machine learning algorithm, using python as core we
can predict the type of crime which will occur in an
area. The objective would be to test a model for
prediction. The training would be done using the
training data set which will be validated using the test
dataset. Building the model will be done using better
algorithm depending upon the accuracy. The
supervised classification and other algorithm will be
used for crime prediction.
1.4 Visualization of dataset is done to analyse the crimes
which may have occurred in the country. This work
helps the law enforcement agencies to predict and
detect crimes in India with improved accuracy and
thus reduces the crime rate. This helps all others
department to carried out other formalities.
2. PROPOSED SYSTEM
2.1 Predictive Model
Predictive modeling is the way of building a model that
can make predictions. The process includes a machine
learning algorithm that learns certain properties from a
training dataset in order to make those predictions.
The different types of predictive models are
a. Decision Trees - A decision tree is an algorithm
that uses a tree shaped graph or model of
decisions including chance event outcomes, costs,
and utility. It is one way to display an algorithm. It
builds classification or regression models in the
form of a tree structure. It breaks down a data set
into smaller and smaller subsets while at the same
time an associated decision tree is incrementally
developed.
b. Support Vector Machines - A classifier that
categorizes the data set by setting an optimal

hyper plane between data. I chose this classifier
as it is incredibly versatile in the number of
different kernelling functions that can be applied,
and this model can yield a high predictability rate.
c. Logistic Regression - Logistic regression is a
Machine Learning classification algorithm that is
used to predict the probability of a categorical
dependent variable. In logistic regression, the
dependent variable is a binary variable that
contains data coded as 1 or 0
d. K-Nearest Neighbor - K-Nearest Neighbor is a
supervised machine learning algorithm which
stores all instances correspond to training data
points in n-dimensional space. When an unknown
discrete data is received, it analyzes the closest k
number of instances saved (nearest neighbors)
and returns the most common class as the
prediction and for real-valued data it returns the
mean of k nearest neighbors.
e. Random forests - Random forests or random
decision forests are an ensemble learning method
for classification, regression and other tasks, that
operate by constructing a multitude of decision
trees at training time and outputting the class that
is the mode of the classes (classification) or mean
prediction (regression) of the individual trees.
Random decision forests correct for decision
trees’ habit of over fitting to their training set.
Random forest is a type of supervised machine
learning algorithm based on ensemble learning.
2.2 Functional Diagram of Proposed Work
It can be divided into 4 parts:
a. Data processing and cleaning
b. Random sampling
c. Train model
d. Estimate the performance
Fig -1: Functional Diagram
a. DATA PROCESSING AND CLEANING
In this step we need prepare data into right format for
analysis and cleaning. We may need to transform the
variables using one of the approaches
1. Normalization or standardization
2. Missing Value Treatment
b. RANDOM SAMPLING
Training Sample: Model will be developed on this sample.
70% or 67% of the data goes here.
Test Sample: Model performances will be validated on this
sample. 30% or 33% of the data goes here.
c. TRAIN MODELS
Validate the assumptions of the chosen algorithm.
Develop/Train Model on Training Sample, which is the
available data and check Model performance - Error,
Accuracy, etc.
d. ESTIMATE THE PERFORMANCE
Score and Predict using Test Sample and check Model
Performance: Accuracy, Error, Precision etc.
3. IMPLEMENTATION
In the first step of accumulating information, data from
previously available/ current datasets from online sources
are gathered together. These datasets are merged to form
a common dataset, on which analysis will be done.
3.1 Data collection
The data set collected for predicting crimes is split into
Training set and Test set. Generally, 7:3 ratios are applied
to split the Training set and Test set. The Data Model,
which was created using Random Forest, logistic, Decision
tree algorithms, K-Nearest Neighbor (KNN) and Support
vector classifier (SVC) are applied on the Training set and
based on the test result accuracy, Test set prediction is
done.
Fig -2: Dataset Description

3.2 Data Preprocessing
This process includes methods to remove any null values
or infinite values which may affect the accuracy of the
system. The main steps include: Formatting, cleaning and
sampling. Cleaning process is used for removal or fixing of
some missing data there may be data that are incomplete.
Sampling is the process where appropriate data are used
which may reduce the running time for the algorithm.
Using python, the preprocessing is done.
3.3 Feature selection
Features selection is done which can be used to build the
model. The attributes used for feature selection are
Dc_Dist, Psa, Dis_date, Dis_time, Hour, User_gen, Pol_dis,
Year, Month and area.
3.4 Training
This method divides dataset into training and test data
randomly in ratio of 67:33 / 70:30. Then we encapsulate
any algorithm. Then we fit our training data into this
algorithm so that computer can get trained using this data.
Now the training part is complete.
3.5 Prediction
The dimensions of new features in a numpy array and the
predict method which takes this array as input and spits
out predicted target value as output. So, the predicted
target value comes out to be 0 or 1. Finally to find the test
score which is the ratio of no. of predictions found correct
and total predictions made and finding accuracy score
method which basically compares the actual values of the
test set with the predicted values.
4. RESULTS AND DISCUSSION
The results are obtained after undergoing various
processes that comes under machine learning.
Data preprocessing - Data preprocessing includes
dropping row without any row and converting any value
which consist of value as infinity. Converting string
variable into numerical so that it can undergo further
processing.
Fig -3: Preprocessed Dataset
After dividing the data set intro training set and testing set
the model is trained using algorithm as mentioned in the
table. The accuracy is calculated using the function
score_accuracy imported from metric from sklearn. The
accuracy is mentioned in the table below.
Fig -4: Results
As we can see from the results obtained from the table the
algorithm which can be used for the predictive modeling
will be Decision Trees or Random Forest algorithms with
accuracy of 98%, the highest among the rest of the
algorithm. The least which can be used will be SVM. For
further modelling using unseen data there is no need for
using other algorithm.
5. CRIME VISUALIZATION
This section deals with the analysis done on the dataset
and plotting them into various graphs like bar, pie, scatter.
Analysis done are
a. crime categories by total areas
b. crime codes in percentage values
c. Relationship diagram for co-relation dataset
columns
d. Classify the crime rate by PSA
e. Classify the crime rate by Year
This graph shows which crimes have occurred most in the
city. The x coordinate denotes the Types of crimes
committed and y coordinate denotes the area code.
Fig -5: Most occurring crimes in the city

The graph below shows the percentage of different crimes
happening in the city. 17.37 % of crime are vandalism and
16.38 % are thefts.
Fig -6: Percentage of different crimes happening in the city
The graph below shows the relationship diagram for co-
relation dataset columns
Fig -7: Relationship diagram for co-relation dataset
columns
The graph below shows the crime rate by PSA. The x
coordinate denotes PSA and y coordinate denotes the
crime rate.
Fig -8: Crime rate by PSA
The graph below shows the crime rate by year. The x
coordinate denotes year and y coordinate denotes the
crime rate.
Fig -9: Crime rate by year
6. CONCLUSIONS
The analytical process started from data cleaning and
processing, missing value, exploratory analysis and finally
model building and evaluation. The best accuracy on
public test set is higher accuracy score of decision tree
algorithm/ Random forest method. This brings some of
the following insights about crime rate. It has become easy
to find out relation and patterns among various data’s. It,
mainly revolves around predicting the type of crime which
may happen if we know the location of where it has
occurred in real time world. Using the concept of machine
learning we have built a model using training data set that
have undergone data cleaning and data transformation.
The model predicts the type of crime with accuracy of 100.
Data visualization generated many graphs and found
interesting statistics that helped in understanding Indian
crimes datasets that can help in capturing the factors that
can help in keeping society safe.
REFERENCES
[1] Shamsuddin, N. H. M., Ali, N. A., & Alwee, R. (2017,
May). An overview on crime prediction methods.
In Student Project Conference (ICT-ISPC), 2017
6th ICT International (pp. 1-5). IEEE.
[2] Al Boni, M., & Gerber, M. S. (2016, December).
Area Specific Crime Prediction Models. In Machine
Learning and Applications (ICMLA), 2016 15th
IEEE International Conference on (pp. 671-676).
IEEE.
[3] Sivaranjani, S., Sivakumari, S., & Aasha, M. (2016,
October). Crime prediction and forecasting in
Tamilnadu using clustering approaches. In
Emerging Technological Trends (ICETT),
International Conference on (pp. 1-6). IEEE.
[4] Sathyadevan, S., & Gangadharan, S. (2014,
August). Crime analysis and prediction using data

mining. In Networks & Soft Computing (ICNSC),
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412). IEEE.
[5] Zhao, X., & Tang, J. (2017, November). Exploring
Transfer Learning for Crime Prediction. In Data
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IEEE.
[6] Tayebi, M. A., Gla, U., & Brantingham, P. L. (2015,
May). Learning where to inspect: location learning
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