Crime prediction and strategy detection in data mining

1. z
Crime prediction
and strategy
detection

2. z
Members
 MD. TUHIN SAUD
 143-15-4614
 M M RAYHAN PARVEZ
 143-15-4617

3. z
CONTENTS
 Objective
 Challenges
 Methodology
 Data Collection

4. z
What is Crime analysis
 Crime analysis is a law enforcement function that involves
systematic analysis for identifying and analyzing patterns and
trends in crime and disorder.

5. z
OBJECTIVE
 Criminal data Visualization
 Strategy Detection
 Efficiency of Criminal datasets
 Finally Crime Prediction

6. z
Motivation
 Detecting crime patterns and perform crime analysis.
 Provide information to formulate strategies for crime prevention and reduction.
 Identify and analyze common crime patterns to reduce further occurrences of
similar incidence.
 To develop a data cleaning algorithm that cleans the crime dataset, by removing
unwanted data
 To explore and enhance classification algorithms to predict future crime behavior
based on previous crime trends.
 To develop anomalies detection algorithms to identify change in crime patterns.

7. z
CHALLEGES
 Increase in the size of Crime Information that has to be stored
and analyzed.
 Problem of Identifying Techniques that can accurately and
efficiently analyze these growing volumes of Crime Data.
 The data available is inconsistent and are incomplete thus
making the task of formal analysis a far more difficult.
 Investigation of the crime takes longer duration due to
complexity of issues.

8. z
METHODOLOGY
Collecting Datasets
Preprocessing data
Correlation
crimes
Analyzing
data
Clustering
cities
Prediction
Visualizing & analyzing the results
Fig1: Methodology for crime patterns analysis

9. z
DATA COLLECTION
 Collect data from police records ends of the year.
 Also collect from national crime bureau records
 Collect data from news portal.
 Sort out data from raw data.

10. z
DATA PREPROCESSING
 Complete Incomplete data using machine learning.
 Find out noisy data and solve the error
 Using method to collect inconsistent data.
 Also clean data, integrate data, transform data.

11. z
CRIME ANALYSIS
 Extraction of crime patterns by analysis of available crime and
criminal data.
 Prediction of crime based on spatial distribution of existing data
and anticipation of crime rate using different data mining
techniques.

12. z
THANK YOU