World Cup Prediction with Deep Learning #851
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
The latest updates on your projects. Learn more about Vercel for Git ↗︎
|
|
👋 Thank you for opening this pull request! We appreciate your contribution to improving this project. Your PR is under review, and we'll get back to you shortly. |
UppuluriKalyani
approved these changes
Nov 10, 2024
|
🎉🎉 Thank you for your contribution! Your PR #851 has been merged! 🎉🎉 |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Related Issues or bug
In sports analytics, accurate prediction of match outcomes remains a challenging task due to the numerous variables influencing game dynamics. Traditional models often rely on general team statistics, which may not capture the nuances of individual player contributions. This project addresses the problem of predicting World Cup outcomes by integrating player-specific data into a Character Embedding deep learning model. The objective is to analyze how player names and historical match data impact prediction accuracy, ultimately enhancing the understanding of feature importance in sports outcome modeling.
Fixes: #848
Proposed Changes
The "World Cup Prediction with Deep Learning" project aims to predict the outcomes of FIFA World Cup matches using historical data and player information, leveraging a Character Embedding model developed in TensorFlow. The primary goal is to use a deep learning approach to achieve approximately 80% accuracy in predicting match results based on specific features, including team rosters and match history. By constructing player embeddings and applying them in a neural network model, this project demonstrates the importance of data features and model architecture in generating predictions for complex, multi-faceted sports events.