Contributors: Amber Curran, Manpreet Dhindsa, Robel Semunegus
Our purpose of analyzing this data set is to detect trends and patterns of the NBA over the last 20 years. We want to understand how teams and players have changed over time and how that may impact future seasons. We will use the data from espn.com and use web scraping to obtain the necessary data. Then, we will organize the data in order to derive different types of analysis. We expect to manipulate data frames and create visualizations to support analyses and trends.
nba.csv - Data set that was used for our project; please note though that web scraping was used to collect data for our analysis but there is a line of code that can be modified to export code to computer but no file directory needed.
CS Project Presentation - Contains presentation visuals from live session video
CS_Final_Project_Code.py - Contains main code used for analysis and query of data set. Please note that code will ask for user input for a player name and year (for a specific NBA season) during the compile/code run. First user input will ask for an NBA basketball and second user input asks for a season year between 2002-2020. Python libraries that need to be installed are numpy, pandas, matplotlib.pyplot, BeautifulSoup (from bs4), and seaborn.
test_CS_Final_Project.py - Test code for CS Semester Project_Final.py
Final Project Report.md - Contains detailed description of our project from data selection to conclusions
finalProjectCode.ipynb - Jupyter Notebook with main code and test code. Please note that code will ask for user input for a player name and year (for a specific NBA season) - user input can be found in section of code prior to test code. Python libraries that need to be installed are numpy, pandas, matplotlib.pyplot, BeautifulSoup (from bs4), and seaborn.