PUBG-clustering-player-behavior-for-cheaters

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PUBG-clustering-player-behavior-for-cheaters

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Given a dataset from Kaggle. EDA + ML was utilized to cluster player behavior to detect cheaters. Deliverables include dashboards and a hacker detection application.

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# PUBG Clustering Player Behavior for Cheaters (Hackers)

## Objective
* Complete Exploratory Data Analysis.
* Utilize Machine Learning to cluster.
* Deploy a dashboard to report findings. (The dashboards are offline. I have conserved costs for other vital applications!)
	* [Dashboard - PART I](https://pubg-eda-part1-dash.herokuapp.com/)  
	* [Dashboard - PART 2](https://pubg-eda-part2-dash.herokuapp.com/)  
	* [Project Summary](https://pubg-hacker-detection-summary.herokuapp.com/)	
* Deploy an application for detecting hackers.
	* [Hacker Detection Application](http://pubg-hd-env.eba-srm42prp.us-east-1.elasticbeanstalk.com/)
		* Please be patient in loading the application.
	
## Background Information
* Playerunknown's Battleground (PUBG) is a video game, which set the standard for preceding games in the Battle Royale genre. The main goal is to SURVIVE at all costs.

---
## Repository Structure
```plaintext
PUBG-clustering-player-behavior-for-cheaters/
├── assets/                                 					# Directory containing asset files
├── data/                                  					# Directory containing data files
├── Animation.gif                           					# GIF demonstrating the project's functionality
├── PUBG_Clustering-AnomalyDetection.ipynb  					# Jupyter Notebook for anomaly detection analysis
├── PUBG_Clustering-DBSCAN.ipynb            					# Jupyter Notebook for DBSCAN clustering analysis
├── PUBG_Clustering-K-means.ipynb           					# Jupyter Notebook for K-means clustering analysis
├── PUBG_Clustering-PCA-AnomalyDetection(All Features).ipynb  			# PCA and anomaly detection on all features
├── PUBG_Clustering-PCA-AnomalyDetection(Selected Features).ipynb  		# PCA and anomaly detection on selected features
├── PUBG_Clustering-PCA-DBSCAN(All Features).ipynb  				# PCA and DBSCAN clustering on all features
├── PUBG_Clustering-PCA-DBSCAN(Selected Features).ipynb  			# PCA and DBSCAN clustering on selected features
├── PUBG_Clustering-PCA-K-means(All Features).ipynb  				# PCA and K-means clustering on all features
├── PUBG_Clustering-PCA-K-means(Selected Features).ipynb  			# PCA and K-means clustering on selected features
├── PUBG_EDA-Dashboard.ipynb                					# Jupyter Notebook for exploratory data analysis dashboard
├── PUBG_Project Summary_Dashboard.ipynb    					# Jupyter Notebook summarizing the project with a dashboard
└── README.md                               					# Overview of the repository
```
---
## Process:
* Exploratory Data Analysis conducted utilizing various python packages (Numpy, Matplotlib, Pandas, and Plotly).
* Clustering Algorithms (Sci-Kit Learn)

## Table of Contents:
* Part I: Exploratory Data Analysis
	* [Jupyter Notebook](https://github.com/SulmanK/PUBG_clustering-player-behavior-for-cheaters/blob/master/PUBG_EDA-Dashboard.ipynb)
	* [Dashboard -EDA PART I](https://pubg-eda-part1-dash.herokuapp.com/)
	* [Dashboard -EDA PART 2](https://pubg-eda-part2-dash.herokuapp.com/)
	
* Part II: Clustering
    * Utilize various clustering algorithms for detecting cheaters or hackers.
    		
		* [Jupyter Notebook: K-means](https://github.com/SulmanK/PUBG_clustering-player-behavior-for-cheaters/blob/master/PUBG_Clustering-K-means.ipynb)
		* [Jupyter Notebook: DBSCAN](https://github.com/SulmanK/PUBG_clustering-player-behavior-for-cheaters/blob/master/PUBG_Clustering-DBSCAN.ipynb)
		* [Jupyter Notebook: Anomaly Detection](https://github.com/SulmanK/PUBG_clustering-player-behavior-for-cheaters/blob/master/PUBG_Clustering-AnomalyDetection.ipynb)
   * Utilize PCA + previous clustering algorithms (All features).
   		* [Jupyter Notebook: K-means](https://github.com/SulmanK/PUBG_clustering-player-behavior-for-cheaters/blob/master/PUBG_Clustering-PCA-K-means(All%20Features).ipynb)
		* [Jupyter Notebook: DBSCAN](https://github.com/SulmanK/PUBG_clustering-player-behavior-for-cheaters/blob/master/PUBG_Clustering-PCA-DBSCAN(All%20Features).ipynb)
		* [Jupyter Notebook: Anomaly Detection](https://github.com/SulmanK/PUBG_clustering-player-behavior-for-cheaters/blob/master/PUBG_Clustering-PCA-AnomalyDetection(All%20Features).ipynb)
   * Utilize PCA + previous clustering algorithms (Selected features).
   		* [Jupyter Notebook: K-means](https://github.com/SulmanK/PUBG_clustering-player-behavior-for-cheaters/blob/master/PUBG_Clustering-PCA-K-means(Selected%20Features).ipynb)
		* [Jupyter Notebook: DBSCAN](https://github.com/SulmanK/PUBG_clustering-player-behavior-for-cheaters/blob/master/PUBG_Clustering-PCA-DBSCAN(Selected%20Features).ipynb)
		* [Jupyter Notebook: Anomaly Detection](https://github.com/SulmanK/PUBG_clustering-player-behavior-for-cheaters/blob/master/PUBG_Clustering-PCA-AnomalyDetection(Selected%20Features).ipynb)
    * Pertinent Deliverables
		* [Hacker Detection Application](http://pubg-hd-env.eba-srm42prp.us-east-1.elasticbeanstalk.com/)

## Demo

![Demo](https://raw.githubusercontent.com/SulmanK/PUBG-clustering-player-behavior-for-cheaters/master/Animation.gif)

* References
	* [1] M. Breunig, H. Kriegel, R. Ng and J. Sander, "LOF", ACM SIGMOD Record, vol. 29, no. 2, pp. 93-104, 2000. Available: 10.1145/335191.335388.
	* [2] "Survey Report on K-Means Clustering Algorithm", International Journal of Modern Trends in Engineering & Research, vol. 4, no. 4, pp. 218-221, 2017. Available: 10.21884/ijmter.2017.4143.lgjzd.
	* [3] A. Drachen, "Introducing Clustering IV: The Case of Tera Online", Gamasutra.com, 2020. [Online]. Available: https://www.gamasutra.com/blogs/AndersDrachen/20140603/218817/Introducing_Clustering_IV_The_Case_of_Tera_Online.php. [Accessed: 02- Mar- 2020].
	* [4] G. News, G. Originals, G. Trailers, G. Guides, M. TV and G. REVIEWS, "Here's How Many PUBG Cheaters Have Been Banned", Game Rant, 2020. [Online]. Available: https://gamerant.com/playerunknowns-battlegrounds-cheater-ban-count/. [Accessed: 02- Mar- 2020].
	* [5] P. Rousseeuw and K. Driessen, "A Fast Algorithm for the Minimum Covariance Determinant Estimator", Technometrics, vol. 41, no. 3, pp. 212-223, 1999. Available: 10.1080/00401706.1999.10485670.
	* [6] S. Ounacer, H. Ait El Bour, Y. Oubrahim, M. Ghoumari and M. Azzouazi, "Using Isolation Forest in anomaly detection: the case of credit card transactions", Periodicals of Engineering and Natural Sciences (PEN), vol. 6, no. 2, p. 394, 2018. Available: 10.21533/pen.v6i2.533.
	* [7] A. Daffertshofer, C. Lamoth, O. Meijer and P. Beek, "PCA in studying coordination and variability: a tutorial", Clinical Biomechanics, vol. 19, no. 4, pp. 415-428, 2004. Available: 10.1016/j.clinbiomech.2004.01.005.

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