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This is Final Capstone Project for ALY15 80625 Intermediate Analytics SEC 04 Spring 2021 CPS. Primarily to learn Data Analytics, Descriptive and Inferential Stastics using R.

License: GNU General Public License v3.0

CSS 3.37% RMarkdown 96.63%
data-analytics rstudio rstats nypd-complaint-data-historic nypd-dataset stastistical-sampling predictive-modeling crime-data

nypd_shooting_analytics's Introduction

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Machine Learning Project and Analytics


The Third Watch

NYPD Shooting Analytics

This is Final Capstone Project for ALY15 80625 Intermediate Analytics SEC 04 Spring 2021 CPS.
Primarily to learn Data Analytics, Descriptive and Inferential Stastics using R.
This paper summarizes our preliminary (Exploratory Data Analysis EDA) analysis on the selected dataset, the NYPD shooting incident dataset for this group project. It will share some insightful results from the EDA and descriptive statistics. Further, this paper identifies a set of methods used to answer the business questions on the dataset and justifies those methods using statistics and analytics concepts.
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Our Paper Presentation Slides

Slide 1 Slide 2 Slide 3
Slide 4 Slide 5 Slide 6
Slide 7 Slide 8 Slide 9
Slide 10 Slide 11 Slide 12

Roadmap

See the open issues for a list of proposed features (and known issues)

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

License

Distributed under the GPL v3 License. See LICENSE for more information.

Contact

Team Members:

  1. Aswin Kumar Rajendran
  2. Chaya Kotturesh
  3. Neil Mascarenhas - About me?

Project Link: https://mascarenhasneil.github.io/NYPD_Shooting_Analytics/

References

Click to expand!
  1. Chapter 11 Categorical Predictors and Interactions | Applied Statistics with R. (2020, October 30). NA. https://daviddalpiaz.github.io/appliedstats/categorical-predictors-and-interactions.html
  2. Coding for Categorical Variables in Regression Models | R Learning Modules. (n.d.). NA. Retrieved May 16, 2021, from https://stats.idre.ucla.edu/r/modules/coding-for-categorical-variables-in-regression-models/
  3. GeeksforGeeks. (2020, October 12). Regression with Categorical Variables in R Programming. https://www.geeksforgeeks.org/regression-with-categorical-variables-in-r-programming/
  4. Logit Regression | R Data Analysis Examples. (n.d.). Idre UCLA. Retrieved May 10, 2021, from https://stats.idre.ucla.edu/r/dae/logit-regression/
  5. Quick-R: Generalized Linear Models. (n.d.). Statmethods. Retrieved May 16, 2021, from https://www.statmethods.net/advstats/glm.html
  6. Rungta, K. (2021, April 8). R Random Forest Tutorial with Example. Rungta Blog. https://www.guru99.com/r-random-forest-tutorial.html
  7. The City of New York. (2020, July 15). NYPD Shooting Incident Data (Historic) | NYC Open Data. NYC Open Data. https://data.cityofnewyork.us/Public-Safety/NYPD-Shooting-Incident-Data-Historic-/833y-fsy8
  8. Winston, A., & Winston, A. (2018, January 27). Transparency Advocates Win Release of NYPD “Predictive Policing” Documents. The Intercept. https://theintercept.com/2018/01/27/nypd-predictive-policing-documents-lawsuit-crime-forecasting-brennan/

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