Purpose of the project is to find closest replacement in terms of ability and playing-style if a target player is not acquirable from market or a current player leaves.
Files
- WebScraping.py: Scape original data from a website
- Dimensionality_Reduction.py: Preprocess data and run dimensionality reduction
- Clustering.py: Run clustering algorithm on preprocessed data
- Visulization.py: Visulize the results and conduct analysis
Samples of the clustering results (Player name, Efficiency, Player's Average Shooting Distance)
Group 10:
-Derrick Rose, 23.11, 12.51
-Dwyane Wade, 24.78, 9.90
-LeBron James, 28.58, 12.25
-Russell Westbrook, 22.38, 9.11
Group 18:
-Carmelo Anthony, 22.66, 11.95
-Kevin Durant, 24.95, 14.98
-Kevin Martin, 18.18, 15.25
-Kobe Bryant, 21.39, 13.96
-Monta Ellis, 20.29, 13.69
Group 29:
-Chris Paul, 23.10, 13.71
-Steve Nash, 20.99, 14.27
Group 44:
-Amar'e Stoudemire, 24.59, 8.36
-Blake Griffin, 25.63, 6.72
-Dwight Howard, 28.31, 4.44
-Kevin Love, 28.37, 9.94
-LaMarcus Aldridge, 23.09, 8.77
-Pau Gasol, 25.40, 7.36
-Zach Randolph, 24.43, 6.86