Finding crime patterns in Montgomery County US

Montgomery is a county in the Maryland US located on the east coast Montgomery County, Maryland.

Improvement of various aspects of social life entitles a proactive and reactive analysis. With this in mind I will be looking to to find patterns by performing time, location and crime classification analysis. For this project I will heavily rely on graph visualization as a picture  is worth a thousand words.

Here are some conclusion highlights:

Time analysis:

  • Most of the crimes are committed Tuesday.
  • On 24 hour basis most of the crimes are committed between 7 a.m – 11 p.m.
  • October has the highest crime count.

Classification analysis:

  • Violent/Non-Violent crimes rates are pretty even 42.8%/57.2%.

Location analysis:

  • Cities with highest crime counts are : Silver Spring, Rockville, Gaithersburg.
  • Most of the crimes happen in the street, residence or parking lot.
  • Silver Spring Police District has the highest crime rates.

Click the link to view the complete project. You can view as well the project in nbviewer.

Discover gender and job related patterns in college majors (A Data Science approach)

Choosing a college major is stressful and making sure you make the right decision can often feel like a daunting task.
In this project we will be examining students who graduated in US between 2010 and 2012. The thought process of an individual choosing a major can be different. But we will explore if there are tendencies in choosing a major based on gender. Also we will check if having a major can improve jobs aspects such as employment and income.


Conclusions highlights:

1. Men typically choose majors such as science, technology, engineering or mathematics.
2. Women tend to choose majors such as health, education or social work.
3. Median unemployment rate is at 0.067 below US unemployment rate(8%-10%).
4. Average salary is around $40K within national average salary range of($39k-$42k).
5. 52.2% of the students with college majors are women while 47.7% are men.

Click the link to view the complete project.

You can also view the project via github