All in one NBA analytics inquriy where you can compare players, get career classifications, determine players market value and find out similar player archetypes.
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Updated
Aug 24, 2020 - Python
All in one NBA analytics inquriy where you can compare players, get career classifications, determine players market value and find out similar player archetypes.
Observations and Results of predicting the results of NBA Games using Machine Learning
NBA Dashboard is a game predictor
A collaborative data science project done in Python analyzing the impact of hustle stats on the outcomes of NBA games. Overall, a logistic model proved best at determining game outcome. This project is for the Stat 426 class at Brigham Young University.
J'essaie de répondre à la question : qui est le meilleur joueur de basketball entre LeBron et Jordan ?
As a follow-up report to project 2, we enhanced our Decision Tree Regression Model by introducing ensemble algorithms, such as Bootstrap, Random Forrest, and XGboosting.
Analyzing NBA stats
Interactive Dashboard for NBA stat visualizations
Blazor webassembly client using MudBlazor components and RapidAPI NBA API
Sports Analytics projects
Project folder for all my Jupyter Notebooks and NBA data.
This repository contains CSV files containing comprehensive NBA data spanning from the year 2010 to 2024, offering valuable insights into player statistics, team performances, game outcomes, and more.
NBA Dashboard App - key statistics about NBA teams over 21-22 season: Most points per game, Most assists per game, Most blocks per game, Most steal per game, eFG%/Points Ratio, 3P%/Position, Blocks/Fouls Ratio, Age/mintutes
Collection of linear regression models predicting NBA player's next season salary
Predicting NBA game outcomes using schedule related information. This is an example of supervised learning where a xgboost model was trained with 20 seasons worth of NBA games and uses SHAP values for model explainability.
An analysis of the 2019 NBA finals in the context of all finals games since 1980; completed as a personal project
Collected posts using Pushift’s API from two different NBA subreddits then used NLP to train a binary classifier identifying the fifteen most frequent words between both subreddits in hopes of determining which NBA Player is “The GOAT.”
The analysis of NBA Finals statistics which are likely to lead a Home Team to Win.
Coaching Experience's Effect on Winning
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