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It uses a linear regression model trained on a historical dataset of team performances.\n\n## Features\n\n- **User Input**: Users can input the team's statistical data, such as matches played, wins, draws, losses, goal difference, and points.\n- **Goal Prediction**: The application uses a linear regression model to predict the number of goals scored by the team based on the input data.\n- **Form Validation**: The form ensures that all fields are filled out correctly before submitting the data for prediction.\n\n## Technologies Used\n\n- **Python**: The main programming language used to develop the application.\n- **Flask**: A micro web framework used to build the web application.\n- **Pandas**: A library used for data manipulation and analysis.\n- **Scikit-learn**: A library used to build and train the linear regression model.\n- **HTML/CSS**: Used to build the user interface.\n- **JavaScript**: Used for client-side form validation.\n- **Render**: The platform used for deploying the application.\n\n## How It Works\n\n1. **Dataset Loading**: The historical dataset of team performances is loaded and preprocessed.\n2. **Model Training**: A linear regression model is trained using the historical data.\n3. **User Interface**: Users input the team's statistical data through a web form.\n4. **Prediction**: The input data is normalized and passed to the model to get the goal prediction.\n5. **Result Display**: The predicted number of goals is displayed to the user.\n\n## Data Source\n\nThe statistical data and information can be retrieved from [FBref - Real Madrid Statistics](https://fbref.com/it/squadre/53a2f082/Statistiche-Real-Madrid).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmike014%2Fchampions_league_predictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmike014%2Fchampions_league_predictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmike014%2Fchampions_league_predictor/lists"}