Football Match Outcome Prediction

This project builds a binary classifier to predict football match results (Home Win or Not) using match statistics and metadata. The model is implemented with PyTorch and trained on a real-world dataset.

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🧾 Dataset

• Dataset file: 2021-2022.csv
• Features used:
  • Division (Div)
  • Home Team (HomeTeam)
  • Away Team (AwayTeam)
  • Home Shots on Target (HST)
  • Home Shots (HS)
  • Away Shots on Target (AST)
  • Away Shots (AS)
  • Home Corners (HC)
  • Away Corners (AC)
  • Referee (Referee)
• Target:
  • Full Time Result (FTR), converted to a binary label: Home Win = 1, else 0

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🔧 Data Preprocessing

• Categorical features are label-encoded to convert text labels to numeric.
• Missing values are imputed using the mean strategy.
• Dataset is split into train/test sets (80/20).
• Features and targets are converted to PyTorch tensors.

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🧠 Model Architecture

• Fully connected feed-forward neural network using PyTorch.
• 4 hidden layers, each with ReLU activation.
• Output layer uses Sigmoid activation for binary classification.
• Model input size equals number of features (10 in this case).
• Output size is 1 (probability of Home Win).

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⚙️ Training Details

• Loss function: Binary Cross Entropy Loss (BCELoss)
• Optimizer: Adam with learning rate 0.001
• Training epochs: 100
• Accuracy calculated during training every 100 epochs.

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📈 Evaluation

• After training, the model is evaluated on the test set.
• Metrics:
  • Test loss
  • Test accuracy

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🚀 How to Run

1. Install dependencies

pip install torch pandas scikit-learn

Download and place the dataset CSV file in your local path (update the path in the script).

Run your training script:

python your_training_script.py

📁 File Structure

├── model.py            # Neural network definition (NeuralNetwork class)
├── train.py            # Training and evaluation script (your main code)
├── 2021-2022.csv       # Dataset file (not included here)
└── README.md           # This file

🧑‍💻 Credits

gob 📜 License

MIT License