Using AI and machine learning, we have to design a prediction model which will predict the future on the present values. The crimes includes snatching,robbery terrorist attacks and stealing . The purpose of this report is to give an explanation and understanding of
Crime Rate Detector
Using AI and machine learning, we have to design a prediction model which will predict the future on the present values. The crimes includes snatching,robbery terrorist attacks and stealing .
The purpose of this report is to give an explanation and understanding of a prediction model system and how the significant calculations of various algorithms can be utilized for the prediction model. Using millions of data and different algorithms to train the model in predicting the crime rates. With the help of different machine learning algorithms, the system can predict information. We have also tried to explain the efficiency and accuracy of the model by using different machine learning algorithms.
Crimes are a common social problem affecting the quality of life and the economic growth of a society. It is considered an essential factor that determines whether or not people move to a new city and what places should be avoided when they travel. With the increase of crimes, law enforcement agencies are continuing to demand advanced geographic information systems and new web application approaches to improve crime analytics and better protect their communities. Although crimes could occur everywhere, it is common that criminals work on crime opportunities they face in most familiar areas for them. By providing a technology approach to determine the most criminal hotspots and find the type, location and time of committed crimes, we hope to raise people’s awareness regarding the dangerous locations in certain time periods. Therefore, our proposed solution can potentially help people stay away from the locations at a certain time of the day along with saving lives. In addition, having this kind of knowledge would help people to improve their living place choices. On the other hand, police forces can use this solution to increase the level of crime prediction and prevention. Moreover, this would be useful for police resources allocation. It can help in the distribution of police at most likely crime places for any given time, to grant an efficient usage of police resources. By having all of this /information available, we hope to make our community safer for the people living there and also for others who will travel there.
To implement this system, we needed a programming language that would be easy to go about. Hence, we decided to go with Python as recommended by the literature we studied. In the world of computer science, python is the leading programming language. According to the article “Advantages and Disadvantages of Python Programming Language”, a great amount of software development companies had a preference for using Python programming language as it is user-friendly and has a huge array of features that can be used for programming. Moreover, it is stated that it has different characteristics for software development like interactive, object-oriented, and dynamic. The benefits of using Python are; it has extensive support libraries and integration features that ease the development of Prediction model applications and software/systems in the field of data science.
On the other hand, we needed to understand how algorithms work. With the guidance of our project supervisor, we were able to dig deep into the algorithms for the prediction model as there are several ways to develop this model and we needed to understand them all properly.
We can easily predict the crime.
An interactive dashboard that will track, analyse, monitor, and visually display the data in real-time.
A complete pipeline that will automate the process of prediction from importing the data, cleaning it, and finally predicting the crime
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| services | Miscellaneous | 2 | 5000 | 10000 |
| Total in (Rs) | 10000 |
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