Predicting which class (average, highlighted) player is according to the points given to the characteristics of the players. (Scenario)
📌I used pandas to understand the data.
📌I used seaborn and matplotlib to visualize the data.
📌I used sklearn for many operations such as data preprocessing and classification performance criteria.
📌I used LightGBM for the classification model.
📌I used optuna optimization tool for hyperparameter optimization.
📌I used oversampling method for data imbalance.
The data set consists of information from Scoutium, which includes the features and scores of the football players evaluated by the scouts according to the characteristics of the footballers observed in the matches.
| Sr. | Feature | Description |
|---|---|---|
| 1 | task_response_id | The set of a scout's assessments of all players on a team's roster in a match |
| 2 | match_id | The id of the match |
| 3 | evaluator_id | The id of the evaluator(scout) |
| 4 | player_id | The id of the player |
| 5 | position_id | The id of the position played by the relevant player in that match. 1-Goalkeeper, 2-Stopper, 3-Right-back, 4-Left-back, 5-Defensive midfielder, 6-Central midfield, 7-Right wing, 8-Left wing, 9-Attacking midfielder, 10-Striker |
| 6 | analysis_id | A set containing a scout's attribute evaluations of a player in a match |
| 7 | attribute_id | The id of each attribute the players were evaluated for |
| 8 | attribute_value | Value (points) given by a scout to a player's attribute |
| Sr. | Feature | Description |
|---|---|---|
| 1 | task_response_id | The set of a scout's assessments of all players on a team's roster in a match |
| 2 | match_id | The id of the match |
| 3 | evaluator_id | The id of the evaluator(scout) |
| 4 | player_id | The id of the player |
| 5 | potential_label | Label showing the final decision of an observer regarding a player in the match. (target variable) |