Predicting NBA salaries using machine learning through R. Clustering players based on stats to determine player type in an increasingly position-less era of basketball.

The NBA game prediction using multiple classifiers and multiple feature sets along with a comparative analysis of the same. Web scraping techniques are used to scrap around the past 10 years data from the official nba/stats website to perform feature engineering for the classification problem.

⛹️⛹️‍♀️⛹️Collecting NBA player data and predicting player position label ⛹️⛹️‍♀️⛹️

Analysis of voting patterns and their correlation to past candidates performance over the years to determine the probability of current candidates to be crowned the NBAs' Most Valuable Player for season 2019-20

Quantifying NBA player interactions

http://sumitrodatta.shinyapps.io/nba-free-agency-dashboard/

Analyze and Predict NBA Players' Fantasy Scores

predicting contracts for 2020 nba free agents

Carried out predictive analysis on the latest NBA 2020 games data to predict the winners/lossers of the NBA 2020 season. Used linear regression in python to develop a winning equation, matplotlib to develop charts and google api to create heat map.

Machine Learning makes predicting NBA2K player ratings one of the easiest things in the world.

predicting 2020's Most Improved Player, as well as candidates for next year

Utilizing machine learning in attempt to accurately predict which NBA players will end up being named All Stars based on their first two seasons in the league

This project attempts to Predict an NBA player's salary bin through Random Forest Classification. This was an independent class project and the README is an adapted version of the final paper.

Predicting the final seeds of National Basketball Association teams, a Machine Learning approach

my dabbling in R and data analysis, predicting All-Star potential for players in the 2017 draft

This is the notebook of our final project for CMSC320: Introduction to Data Science. In this project, we explore Hall of Famers in the NBA.

Demonstrates the deployment process of FanaticFi, an NBA Draft Rank Prediction app empowered by Support Vector Machines (SVM) machine learning model using the Flask application and Heroku cloud services. This user-friendly web application is going to predict whether NBA rookie’s draft rank is within the top 15 or below based on given player’s st…

Predicting LeBron's Next Era

The project aims to predict the winner of a basketball game, the ranking of the NBA Playoffs, the winner of each conference, as well as the winner of the NBA.