---
# System prepended metadata

title: CZ4032 Data Analytics & Mining Assignment

---


# CZ4032 Data Analytics & Mining Assignment

```
1. Abstract
2. Problem Description
3. Implementation
4. Experimental Results & Analysis
5. Discussion of Pros and Cons
6. Conclusion
7. References
8. Appendix
```

## Abstract

## Problem Description
#### Motivation
Crime has been existing for the longest period of time. In fact, crime rate has been on the rise despite having law and enforcement. This has been a problem especially in cities like Chicago.

However, with the help of technology advancement and readily available crime statictics. We can now leverage on data analytic tools to create predictive algorithm that can be used to lower crime rate. The image below shows how our approach can effectively reduce crime rates

![](https://i.imgur.com/mO3IA8Z.png)
*Figure 1: Crime prevention measures using data analytics [1]
<!-- In order to provide a safe environment for people to live in, data analytics is effective in predicting crime. 

In today's society, crimes remains to be prevalent problem.(Add source)-->

#### Problem Definition
Since the generalized problem of predicting crime is vast, we chose to focus Chicago, which has a reputation of being one of the most violent cities in United States of America. Statistics reveals that in the year of 2016, there were 762 murders, 3,550 shooting incidents and 4,331 shooting victims happened in Chicago [2].




 #### Related Work
https://pdfs.semanticscholar.org/34ad/a04010fdb79c02fd1915e534089c36ab4c67.pdf
http://www.naun.org/main/NAUN/computers/2014/a022007-096.pdf


## Approach
 #### Methodology
 ##### Data Selection
 ##### Data Cleaning & Prepocessing
 
Data Modification:

1. "Date" attribute 
    - Standardize the date & time format to 24hr clock (eg. DD/MM/YY HHMM) - QX
    - Seperate Date & Time into diff columns - Min
2. "Block" attribute
    - Remove the blk number - Elric
3. "Beat" attribute
    - Format figures to 4 digit - Raymond
4. "District" attribute
    - Format figures to 2 digits - Ash


Data Removal:
1. Remove Column A - missing info
2. Remove "Domestic" attribute
3. Remove "Ward" attribute
4. Remove "IUCR" attribute (IUCR = description)
5. Remove "FBI Code" attribute (FBI Code = Primary Type)
6. Remove "X Coordinate" attribute
7. Remove "Y Coordinate" attribute
8. Remove "Year" attribute
9. Remove "Year" attribute
10. Remove "Updated on" attribute
11. Remove "Latitude" attribute
12. Remove "Longtitude" attribute
13. Remove "Location" attribute


 #### Algorithms
## Implementations
## Experimental Result and Analysis
 #### Experimental Setup
 #### Comparison Schemes
 #### Results and Analysis
## Discussion of Pros and Cons
## Conclusions
 #### Summary of project achievements
 #### Direction for improvements

## References
[1]    M. Hvistendahl, "Can ‘predictive policing’prevent crime before it happens," Science Magazine, vol. 28, 2016.

[2]    N. McCarthy, "Is Chicago Really America's Most Dangerous City? [Infographic]", Forbes.com, 2019. [Online]. Available: https://www.forbes.com/sites/niallmccarthy/2017/06/21/is-chicago-really-americas-most-dangerous-city-infographic/#f04d12850da1. [Accessed: 13- Sep- 2019].

[3]    S. Pelley, "Predicting crime in Chicago", Cbsnews.com, 2019. [Online]. Available: https://www.cbsnews.com/news/predicting-crime-in-chicago/. [Accessed: 12- Sep- 2019].



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