Node application using D3 to Analyze Data from the NBA
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Updated
Jun 7, 2017 - HTML
Node application using D3 to Analyze Data from the NBA
NBA 2k and Stats Exploratory Data Analysis and Modeling. View project here: https://htmlpreview.github.io/?https://github.com/willyiamyu/nba2k_analysis/blob/master/nba.html
This repo explores correlations between NBA player salary and on-court performance data, using stats between 2000 to 2022 regular season. It also illustrates how you can conduct web-scraping and simple data analysis in Python.
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A study of the significance of 3-pt efficiency in the NBA for predicting team win % when also considering other player stats in 4 eras: 2000-2004, 2005-2009, 2010-2014, & 2015-2019.
#rstats R project for experimenting with machine learning using NBA data (circa 2016)
A data science and machine learning project that attempts to predict the ideal player attributes for a top NBA Draft pick using supervised learning and a linear regression model, built using the NBA Data API, Python, NumPy, Pandas, and Sci-Kit Learn.
The NBA data and Machine Learning
Web app that leverages Flask backend to pull wealth of data on NBA players in clutch circumstances directly from NBA.com and visualizes it in a comprehensible manner, allowing users to settle the age-old argument: who's more clutch?
Analysis of different statistics that could be used to determine a player's ranking in the MVP voting race
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