Data-Science-Blog-Post-DSND-Udacity-project

Licensing, Authors, and Acknowledgements

Installation

Project Motivation

File Descriptions

Results

Licensing, Authors, and Acknowledgements

Installation:

Python versions 3.*.

• Libraries:
• Pandas.
• Scikit-learn.
• numpy.
• matplotlib.
• seaborn.

Project Motivation:

This project usedFBI Gun Data I was interested in working for a dataset I used before. This dataset comes from the FBI's National Instant Criminal Background Check System. The NICS is used to determine whether a prospective buyer is eligible to buy firearms or explosives. Gun shops call into this system to ensure that each customer does not have a criminal record or isn’t otherwise ineligible to make a purchase. The data has been supplemented with state-level data from. I analyzed this dataset to find interesting outcomes and find interesting results. I asked and answered for these:

• The highest rate of Hispanic or Latino
• The rate of handguns on each state.
• The month which occurred the largest rate of long gun purchases.
• The state with least affected by a total of purchase of guns.
• The highest gun purchase for each prospective buyer.

File Descriptions:

To complete this project, you will use Jupyter Notebooks through a workspace in the classroom. Jupyter Notebooks are a great way to work with your code interactively while also being able to include descriptive and informative text to build a report. The next few concepts in this lesson will help you get started with understanding notebooks. If you would like to learn more about these tools, or you need some additional help to get started, you can check out Anaconda and Jupyter Notebooks in the "Intro to Data Analysis" Core Concept of this course.

Results:

The main findings of the code can be found here.

Licensing, Authors, and Acknowledgements:

The data in this repository comes from the FBI's National Instant Criminal Background Check System. github