nba players clustering based on pure nba stats.

data source from stat.nba.com

json(see get_data.py)
api(see jupyter notebook files). It provides better data for the task of clustering of nba players.

For players, tsne method is used for clustering.
Similarity between teams is shown in hierarchy structure(only demonstrated in jupyter notebook).

Name		Name	Last commit message	Last commit date
Latest commit History 8 Commits
.ipynb_checkpoints		.ipynb_checkpoints
__pycache__		__pycache__
python_notebook		python_notebook
README		README
README.md		README.md
data_processing.py		data_processing.py
draw.R		draw.R
get_data.py		get_data.py
nba_centers_2013.csv		nba_centers_2013.csv
nba_centers_2014.csv		nba_centers_2014.csv
nba_centers_2015.csv		nba_centers_2015.csv
nba_norm_2013.csv		nba_norm_2013.csv
nba_norm_2014.csv		nba_norm_2014.csv
nba_norm_2015.csv		nba_norm_2015.csv
nba_player_similarity 2013 .html		nba_player_similarity 2013 .html
nba_player_similarity 2014 .html		nba_player_similarity 2014 .html
nba_player_similarity 2015 .html		nba_player_similarity 2015 .html
nba_projected_2013.csv		nba_projected_2013.csv
nba_projected_2014.csv		nba_projected_2014.csv
nba_projected_2015.csv		nba_projected_2015.csv
nba_raw_2013.csv		nba_raw_2013.csv
nba_raw_2014.csv		nba_raw_2014.csv
nba_raw_2015.csv		nba_raw_2015.csv
player_cluster.py		player_cluster.py