CRIME PREDICTION AND ANALYSIS USING MACHINE LEARNING

This system can predict regions which have high probability for crime occurrences and visualize crime prone areas.

1. DEPARTMENT OF COMPUTER SCIENCE ENGINEERING (DATA SCIENCE) CRIME PREDICTION AND ANALYSIS USING MACHINE LEARNING DONE BY BATCH – MP04 GEETHIKA PUTTI – 207Y1A6712 RANKIREDDY SATISH – 207Y1A6743 BONAGIRI VINAY – 207Y1A6759 UNDER THE GUIDANCE OF Mr.A.VEERABABU ASSISTANT PROFESSOR HOD OF CSD DR.N. PUSHPALATHA

2. PROBLEM DEFINITION 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. Crime prediction and criminal identification are the major problems to the police department as there are tremendous amounts of crime data that exist. There is a need for technology through which case solving could be faster. This made me go for research about how to solve a crime case made easier. Through many documentations and cases, it came out that machine learning and data science can make the work easier and faster.

3. SCOPE OF THE PROJECT Crime analysis and prediction is a systematic approach for identifying the crime. This system can predict regions which have high probability for crime occurrences and visualize crime prone areas. Using the concept of machine learning we can extract previously unknown, useful information from an unstructured data.

4. EXISTING SYSTEM Many researchers have gone through this problem regarding the criminal cases being unsolved for a long period. They proposed different crime prediction algorithms. In all these models the accuracy will surely vary depending on the data set and the features or attributes we select during data pre-processing. In Crime prediction done on the Mississippi crime data set where models like linear regression and Decision stump models are used gave a result of 83%, 88% and 67% respectively. Although these accuracies of the predictions may vary accordingly because it is discovered that many machine learning algorithms are implemented on data sets consisting of different places having distinctive features, so predictions are changing in all cases.

5. PROPOSED SYSTEM Nearly all of the crimes are predicted based on the location and the types of crimes that are occurring in those areas. On Surveying previous works, Linear Regression, Decision Tree and Random Forest tend to predict crimes. The dataset contains different types of crimes that being committed in India According to the state and year respectively.

6. SYSTEM REQIREMENTS HARDWARE REQUIREMENTS SOFTWARE REQIUREMWNTS

7. SYSTEM DESIGN

8. DESIGNING DIAGRAMS CLASS DIAGRAM

9. DESIGNING DIAGRAMS SEQUENCE DIAGRAM

10. CONCLUSION Crimes are serious threats to human society, safety, and sustainable development and are thus meant to be controlled. Investigation authorities often demand computational predictions and predictive systems that improve crime analytics to further enhance the safety and security of cities and help to prevent crimes.