# Player Valuer — XGBoost
An end-to-end machine learning model for estimating football player market value from seasonal performance and profile features.
The model uses an XGBoost regressor wrapped in a scikit-learn preprocessing pipeline.
## Model Inputs
- Age
- Position: GK / DF / MF / FW
- Minutes played
- Goals
- Assists
- Yellow cards
- Red cards
- Average rating
- Contract years remaining
## Output
Estimated player market value in EUR.
## Pipeline
Numerical features:
- Median imputation
- Standard scaling
Categorical features:
- Most-frequent imputation
- One-hot encoding
Regression:
- XGBoost
## Important Note
This model was trained on synthetically generated football-player data and is intended as an educational machine learning project and portfolio demonstration.
Predictions should not be interpreted as real-world player valuations.
## Source Code
GitHub: https://github.com/enesor0/valuer
