https://github.com/mike014/champions_league_predictor
This web application predicts the number of goals scored by a team in the Champions League based on various statistical parameters. It uses a linear regression model trained on a historical dataset of team performances.
https://github.com/mike014/champions_league_predictor
flask linerregression multiple-linear-regression python
Last synced: 4 months ago
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This web application predicts the number of goals scored by a team in the Champions League based on various statistical parameters. It uses a linear regression model trained on a historical dataset of team performances.
- Host: GitHub
- URL: https://github.com/mike014/champions_league_predictor
- Owner: Mike014
- License: gpl-3.0
- Created: 2024-09-16T18:22:16.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-02T09:02:09.000Z (over 1 year ago)
- Last Synced: 2025-08-16T22:32:22.490Z (10 months ago)
- Topics: flask, linerregression, multiple-linear-regression, python
- Language: Python
- Homepage: https://champions-league-predictor.onrender.com/
- Size: 37.1 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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README
# Predicting Champions League Winner [](https://github.com/sponsors/Mike014)
## Description
This web application predicts the number of goals scored by a team in the Champions League based on various statistical parameters. It uses a linear regression model trained on a historical dataset of team performances.
## Features
- **User Input**: Users can input the team's statistical data, such as matches played, wins, draws, losses, goal difference, and points.
- **Goal Prediction**: The application uses a linear regression model to predict the number of goals scored by the team based on the input data.
- **Form Validation**: The form ensures that all fields are filled out correctly before submitting the data for prediction.
## Technologies Used
- **Python**: The main programming language used to develop the application.
- **Flask**: A micro web framework used to build the web application.
- **Pandas**: A library used for data manipulation and analysis.
- **Scikit-learn**: A library used to build and train the linear regression model.
- **HTML/CSS**: Used to build the user interface.
- **JavaScript**: Used for client-side form validation.
- **Render**: The platform used for deploying the application.
## How It Works
1. **Dataset Loading**: The historical dataset of team performances is loaded and preprocessed.
2. **Model Training**: A linear regression model is trained using the historical data.
3. **User Interface**: Users input the team's statistical data through a web form.
4. **Prediction**: The input data is normalized and passed to the model to get the goal prediction.
5. **Result Display**: The predicted number of goals is displayed to the user.
## Data Source
The statistical data and information can be retrieved from [FBref - Real Madrid Statistics](https://fbref.com/it/squadre/53a2f082/Statistiche-Real-Madrid).