This is a data mining project that aims to mine real footballing data to extract useful knowledge using data mining and machine learning techniques. There are 4 main sections in this report: Problem descriptions, Dataset and preprocessing, Algorithms, Results and analysis. The overall data mining procedures and techniques used are also introduced.
Therefore, the objective of this project is to use real in-match statistics to predict the market value of the football player in € in the sense that we can analyze the goodness as well as the level of the football players.
- FBref.com
- Transfermarkt.com
- Neural network
- Ridge regression
- ElasticNet
- Principal Component Analysis (PCA)
- Autoencoder
- Feature Selection
| Syntax | MAE mean | MAE min | RMSE mean | RMSE min |
|---|---|---|---|---|
| PCA+NN (Without log transformation) | 7.55715625 | 7.0544 | 12.46855625 | 11.4937 |
| Autoencoder + NN (Without log transformation) | 8.480625 | 7.1313 | 12.36180625 | 7.3327 |
| PCA+NN (With log transformation) | 7.85724375 | 6.363 | 14.41860625 | 11.1701 |
| Autoencoder + NN (With log transformation) | 8.4075875 | 7.0791 | 13.9653375 | 8.7817 |
| Ridge Regression | 6.3427 | 5.8817 | 11.6661 | 10.0278 |
| ElasticNet Regression | 6.41438 | 5.9112 | 11.6596 | 10.4843 |