#Stat 133-final-project

NBA Analytics

Description: Contains the files required for the statistics (133) final project in Fall 2016. Using basic machine learning techniques, we characterized players' value by position and assessed their value (per dollar) to each team.

For quick look at final data, view 'finalInterestingData' directory for best players sorted by overall value and value per dollar. Additionally, look at these links for awesome visualizations and insights into interesting trends in the NBA.

Stat-Salaries: https://canishka.shinyapps.io/stat-salaries/ Team-Salaries: https://canishka.shinyapps.io/team-salaries/

Authors: Girish Balaji, Rachel Lee, Yian Liou, Canishka De Silva

File Structure: File structure is similar to what is in the Open Source Framework (OSF) Structure requirements that were given by the Professor.

Notes: The data scraping,download data,and clean data scripts get the NBA data and clean it from the website, after which the eda-script generates summary statistics for each variable and puts it in a text file and saves plots of each variable under images. then, compuete efficiency calculates each player's value and notes which players are the best and worst.

The shiny apps illustrate the team salaries statistics and efficiency values for each player.

This work is licensed under the MIT License.