To be better prepared to respond to criminal activity, it is important to understand patterns in crime. In our project, we analyze crime data from the city of Indore, scraped from publicly available website of Indore Police. At the outset, the task is to predict which category of crime is most likely to occur given a time and place in Indore. The use of AI and machine learning to detect crime via sound or cameras currently exists, is proven to work, and expected to continue to expand. The use of AI/ML in predicting crimes or an individual’s likelihood for committing a crime has promise but is still more of an unknown. The biggest challenge will probably be “proving” to politicians that it works. When a system is designed to stop something from happening, it is difficult to prove the negative. Companies that are directly involved in providing governments with AI tools to monitor areas or predict crime will likely benefit from a positive feedback loop. Improvements in crime prevention technology will likely spur increased total spending on this technology. We also attempt to make our classification task more meaningful by merging multiple classes into larger classes. Finally, we report and reflect on our results with different classifiers, and dwell on avenues for future work.

1.1 Background of the study
Crime, in a way, influences organizations and institutions when occurred frequently in a society. Thus, it is necessary to study the factors and relations between different crimes and to find a way to accurately predict and avoid these crimes. Recently law enforcement agencies have been moving towards a more empirical, data driven approach to predictive policing. However, even with new data-driven approaches to predict crime, the fundamental job of crime analysts still remains difficult and often manual; specific patterns of crime are not very easy to find by way of automated tools, whereas larger-scale density-based trends comprised mainly of background crime levels are much easier for data-driven approaches and software to estimate.

With the advent of the Big Data era and the availability of fast, efficient algorithms for data analysis, understanding patterns in crime from data is an active and growing field of research.

Crimes are the significant threat to the humankind. There are many crimes that happen in regular intervals of time. Perhaps it is increasing and spreading at a fast and vast rate. Crimes happen from small village, town to big cities. Crimes are of different type – 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. 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.

Through many documentation and cases, it came out that machine learning and data science can make the work easier and faster. The inputs to our algorithms are time (hour, day, month, and year), place (latitude and longitude), and class of crime:

• Act 379 - Robbery

• Act 13 - Gambling

• Act 279 - Accident

• Act 323 - Violence

• Act 302 - Murder

• Act 363 - Kidnapping

The output is the class of crime that is likely to have occurred. We try out multiple classification algorithms, such as KNN (K-Nearest Neighbors), Decision Trees, and Random Forests.

We also perform multiple classification tasks – we first try to predict which of 6 classes of crimes are likely to have occurred, and later try to differentiate between violent and non-violent crimes.

1.2 Statement of the problem
The main problem is that day to day the population is going to be increased and by that the crimes are also going to be Increased in different areas by this the crime rate cannot be accurately predicted by the officials. The officials as they focus on many issues may not predict the crimes to be happened in the future. The officials/police officers although they try to reduce the crime rate they may not reduce in full-fledged manner. The crime rate prediction in future may be difficult for them.

There has been countless of work done related to crimes. Large datasets have been reviewed, and information such as location and the type of crimes have been extracted to help people follow law enforcements. Existing methods have used these databases to identify crime hotspots based on locations.

Even though crime locations have been identified, there is no information available that includes the crime occurrence date and time along with techniques that can accurately predict what crimes will occur in the future.

Our study aims to find spatial and temporal criminal hotspots using a set of real-world datasets of crimes. We will try to locate the most likely crime locations and their frequent occurrence time. In addition, we will predict what type of crime might occur next in a specific location within a particular time. Finally, we intend to provide an analysis study by combining our findings of a particular crimes dataset with its demographics information.

1.3 Rationale
Madhya Pradesh's commercial capital Indore has topped the crime record in the country in 2008 followed by Bhopal and Jaipur. Crime rate of Indore was 941.4, which is the highest in the country, according to National Crime Record Bureau's (NCRB) report - "Crime in India 2008".

With the rapid urbanization and development of big cities and towns, the graph of crimes is also on the increase. This phenomenal rise in offences and crime in cities is a matter of great concern and alarm to all of us.

There are robberies, murders, rapes and what not. The frequent and repeated thefts, burglaries, robberies, murders, killings, rapes, shoplifting, pick pocketing, drug- abuse, illegal trafficking, smuggling, theft of vehicles etc., have made the common citizens to have sleepless nights and restless days.

They feel very insecure and vulnerable in the presence of anti-social and evil elements. The criminals have been operating in an organized way and sometimes even have nationwide and international connections and links.

1.4 Goal
Much of the current work is focused in two major directions:

• Predicting surges and hotspots of crime, and

• Understanding patterns of criminal behavior that could help in solving criminal investigations.

1.5 Objective
The objective of our work is to:

• Predicting crime before it takes place.

• Predicting hotspots of crime.

• Understanding crime pattern.

• Classify crime based on location.

• Analysis of crime in Indore.

1.6 Methodology
1.6.1 Machine learning
The term machine learning refers to the automated detection of meaningful patterns in data. In the past couple of decades it has become a common tool in almost any task that requires information extraction from large data sets.We are surrounded by a machine learning based technology: search engines learn how to bring us the best results (while placing pro_table ads), anti-spam software learns to filter our email messages, and credit card transactions are secured by a software that learns how to detect frauds. Digital cameras learn to detect faces and intelligent personal assistance applications on smart-phones learn to recognize voice commands. Cars are equipped with accident prevention systems that are built using machine learning algorithms.

Machine learning is also widely used in scientific applications such as bioinformatics, medicine, and astronomy. One common feature of all of these applications is that, in contrast to more traditional uses of computers, in these cases, due to the complexity of the patterns that need to be detected, a human programmer cannot provide an explicit, finedetailed specification of how such tasks should be executed. Taking example from intelligent beings, many of our skills are acquired or re_ned through learning from our experience (rather than following explicit instructions given to us). Machine learning tools are concerned with endowing programs with the ability to learn and adapt

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Item Type: Project Material  |  Size: 43 pages  |  Chapters: 1-5
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