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This repository contains project materials for the Fall 2023 MGT 256 class. This project is completed with assists from Professor Adem Orsdemir.

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Jen-uis/LA-Crime-Data-Analysis

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LA-Crime-Data-Analysis

This repository contains project materials for the Fall 2023 MGT 256 class. This project is completed with assists from Professor Adem Orsdemir.

Introduction

For those who are new to this folder, the Project-Code.rmd, Project-Code.html, and Project-Code.Rproj files are our main coding files. This project is made possible with R Studio using R Markdown. The data is originally obtained from Data.gov, link will be attached below. Feel free to explore more options beyond this analysis report.

Project Idea

This project focuses on analyzing crime data in Los Angeles to identify patterns and trends. By leveraging various data analysis techniques, we aim to provide insights into crime distribution across different neighborhoods, the types of crimes most prevalent in specific areas, and temporal patterns such as monthly or yearly trends. The goal is to assist local authorities and policymakers in understanding the crime landscape better, enabling them to implement more effective crime prevention strategies and resource allocation.

Contents

  • Project-Code.rmd: R Markdown file which contains the majority of the codes.
  • Project-Code.html: HTML export of the R Markdown for easy viewing.
  • Project-Code.Rproj: Main R Project file to use for R Studio.
  • Paper-Report.docs: Documents which reports our finding and analysis during the project.
  • Final-Presentation.ppt: Presentation slides that include our final findings and analysis.
  • Data Folder: Contains the datasets used for analysis. Disclaimer: The data is obtained from Data.gov Crime Data from 2020 to Present published by City of Los Angeles. All data are used for educational purposes only.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contributing

This project had been finished at December 2023, no changes shall be made to this main repository. No edits will be approved.

Contact

If you have any questions or need further information, please contact our team at: connectnathaniel@gmail.com

Authors:

  • Leader: Nathaniel Zhu
  • Youyi Fu (Chris)
  • Haiyin Lin (Jane)
  • Mao-hua Wu (Moris)

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This repository contains project materials for the Fall 2023 MGT 256 class. This project is completed with assists from Professor Adem Orsdemir.

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