https://github.com/Omaraitbenhaddi/ODC-World-Cup-2022-Predictions

Predict who will win the FIFA World Cup 2022
https://github.com/Omaraitbenhaddi/ODC-World-Cup-2022-Predictions

django fifa machine-learning worldcup2022 xgboost

Last synced: 25 days ago
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Predict who will win the FIFA World Cup 2022

• Host: GitHub
• URL: https://github.com/Omaraitbenhaddi/ODC-World-Cup-2022-Predictions
• Owner: Omaraitbenhaddi
• License: apache-2.0
• Created: 2022-09-21T08:39:07.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2022-11-19T21:44:59.000Z (over 1 year ago)
• Last Synced: 2024-05-17T23:38:02.632Z (about 2 months ago)
• Topics: django, fifa, machine-learning, worldcup2022, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 5.19 MB
• Stars: 8
• Watchers: 2
• Forks: 2
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: license

Lists

• awesome-morocco - ODC-World-Cup-2022-Predictions - World-Cup-2022-Predictions.svg?style=social)](https://github.com/Omaraitbenhaddi/ODC-World-Cup-2022-Predictions/stargazers) - Prediction of the winner of an international matches Prediction results are "Win / Lose / Draw" and Apply the model to predict the result of FIFA world cup 2022 by [@Omaraitbenhaddi](https://github.com/Omaraitbenhaddi) (Uncategorized / Uncategorized)

README

        
# ODC-World-Cup-2022-Predictions

# Project Description

__Objective__:

- Prediction of the winner of an international matches Prediction results are  "Win / Lose / Draw" 

- Apply the model to predict the result of FIFA world cup 2022.

__Data__: 

          -Nous avons les rangs FIFA de 1993 à 2018 donnés par 

                          https://www.kaggle.com/datasets/tadhgfitzgerald/fifa-international-soccer-mens-ranking-1993now

          - Nous avons les rangs FIFA de 1992-2022 

                          https://www.kaggle.com/datasets/cashncarry/fifaworldranking

          -L’historique des matches de football depuis 1872 donné par 

                          https://www.kaggle.com/datasets/martj42/international-football-results-from-1872-to-2017

         -Les statistiques de chaque équipe depuis 2018 tirées de Wikipédia                      

                          https://en.wikipedia.org/wiki/National_team_appearances_in_the_FIFA_World_Cup#Overall_team_records

         -Les statistiques des joueurs tirées de 

                          https://www.kaggle.com/antoinekrajnc/soccer-players-statistics

         -Fifa index            

                          https://www.fifaindex.com/fr/team/1335/france/fifa23/

         -Football nations Stats  

                          https://fbref.com/en/countries/

          -Football nations Stats  https://fbref.com/en/countries/

           -Players data to scrap https://fbref.com/en/players/e42d61c7/Achraf-Hakimi

__Environment and tools__

          1. Jupyter Notebook 

          2. Numpy

          3. Pandas

          4. Seaborn

          5. Matplotlib 

          6. Scikit-learn

          7. xgboost 

          8. scipy

          9. joblib

we chose XGBoost in  model and got an accuracy of 78% on the training set and 63% accuracy on the test set

__Lifecycle__

![](https://github.com/mrthlinh/FIFA-World-Cup-Prediction/blob/master/pic/life_cycle.png)

    

__site web__

          

         https://world-cup-2022-predictions.herokuapp.com/