sports-analysis

There are 0 repository under sports-analysis topic.

• ascuet / SoccerAct10

  SoccerAct10 is a dataset which contains 10 different soccer actions. This dataset was developed using the videos from YouTube.

  action-recognitionaction-recognition-datasetfine-grained-classificationvideo-action-recognitionvideo-classificationvideo-datasetsoccer-activity-classificationsoccer-video-classificationsports-classificationsports-recognition-datasetsports-video-classificationsports-analysiskeras-action-recognitionpytorch-action-recognitionfootball-datasetfootball-action-classificationfootball-action-recognition
  87

• huzaifakhan04 / exploratory-data-analysis-of-european-football-database-using-sql-and-python

  This repository contains results of an exploratory data analysis with visualisation performed on nearly 11,000,000 entries across the European Soccer (Football) Database, based in the SQLite database engine, from Kaggle using SQL (SQLite) in Python.

  data-analysisdata-sciencedata-visualisationdatabaseedaeuropean-football-leaguesexploratory-data-analysisfootball-analyticssoccer-analyticssqlsqlitesqlite3sports-analysis
  Language:Jupyter Notebook 1

• shishir349 / Exploring-the-world-of-Football

  FIFA 19 is a football simulation video game developed by EA Vancouver as part of Electronic Arts' FIFA series. Announced on 6 June 2018 for its E3 2018 press conference, it was released on 28 September 2018 for PlayStation 3, PlayStation 4, Xbox 360, Xbox One, Nintendo Switch, and Microsoft Windows.It is the 26th installment in the FIFA series. As with FIFA 18, Cristiano Ronaldo initially as the cover athlete of the regular edition: however, following his unanticipated transfer from Spanish club Real Madrid to Italian side Juventus, new cover art was released, featuring Neymar, Kevin De Bruyne and Paulo Dybala.

  sportsdata-analysisdata-sciencedata-visualiationsquery-analysisadhoc-analysisdata-miningmachine-learningsports-analysiscomparative-analysissupervised-machine-learning
  Language:Jupyter Notebook 1

• ffalves / ordinary_methods

  Using Ordinary Methods from Research Operations to choose the best player!

  football-analysisresearch-operationssoccer-analysissports-analysisordinary-methods
  Language:R 0

• shraddhapattnaik / epl-match-winner-prediction

  Predicting match winners in the English Premier League using web scraping, data cleaning, and machine learning in Python.

  beautifulsoupdata-sciencejupyter-notebookmachine-learningpandaspremier-leaguepythonskit-learnsports-analysisweb-scraping
  Language:Jupyter Notebook 0

• SimranKaur-23 / Sports-analysis

  Sports analysis on the given dataset

  analysisedafootballpythonsportssports-analysisvisualization
  Language:Jupyter Notebook 0