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@ -13,79 +13,110 @@ def LogisticRegression_predict_proba(position_x, position_y, distance_to_goalM,
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return model.predict_proba(X_new)[0][1].round(2)
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#xgBoost
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def xgboost_predict_proba(minute, position_name, shot_body_part_name, shot_technique_name,
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shot_type_name, shot_first_time, shot_one_on_one,
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shot_aerial_won, shot_deflected, shot_open_goal,
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shot_follows_dribble, shot_redirect, x1, y1,
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number_of_players_opponents, number_of_players_teammates,
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angle, distance, x_player_opponent_Goalkeeper,
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x_player_opponent_8, x_player_opponent_1, x_player_opponent_2,
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x_player_opponent_3, x_player_teammate_1, x_player_opponent_4,
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x_player_opponent_5, x_player_opponent_6, x_player_teammate_2,
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x_player_opponent_9, x_player_opponent_10, x_player_opponent_11,
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x_player_teammate_3, x_player_teammate_4, x_player_teammate_5,
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x_player_teammate_6, x_player_teammate_7, x_player_teammate_8,
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x_player_teammate_9, x_player_teammate_10,
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y_player_opponent_Goalkeeper, y_player_opponent_8,
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y_player_opponent_1, y_player_opponent_2, y_player_opponent_3,
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y_player_teammate_1, y_player_opponent_4, y_player_opponent_5,
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y_player_opponent_6, y_player_teammate_2, y_player_opponent_9,
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y_player_opponent_10, y_player_opponent_11, y_player_teammate_3,
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y_player_teammate_4, y_player_teammate_5, y_player_teammate_6,
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y_player_teammate_7, y_player_teammate_8, y_player_teammate_9,
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y_player_teammate_10, x_player_opponent_7, y_player_opponent_7,
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x_player_teammate_Goalkeeper, y_player_teammate_Goalkeeper,
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shot_kick_off):
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def xgboost_predict_proba(minute=0, position_name='Center Forward', shot_body_part_name='Right Foot',
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shot_technique_name='Normal', shot_type_name='Open Play', shot_first_time=False,
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shot_one_on_one=False, shot_aerial_won=False, shot_deflected=False,
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shot_open_goal=False, shot_follows_dribble=False, shot_redirect=False, x1=0.0, y1=0.0,
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number_of_players_opponents=0, number_of_players_teammates=0,
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angle=0.0, distance=0.0, x_player_opponent_Goalkeeper=np.nan,
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x_player_opponent_8=np.nan, x_player_opponent_1=np.nan, x_player_opponent_2=np.nan,
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x_player_opponent_3=np.nan, x_player_teammate_1=np.nan, x_player_opponent_4=np.nan,
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x_player_opponent_5=np.nan, x_player_opponent_6=np.nan, x_player_teammate_2=np.nan,
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x_player_opponent_9=np.nan, x_player_opponent_10=np.nan, x_player_opponent_11=np.nan,
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x_player_teammate_3=np.nan, x_player_teammate_4=np.nan, x_player_teammate_5=np.nan,
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x_player_teammate_6=np.nan, x_player_teammate_7=np.nan, x_player_teammate_8=np.nan,
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y_player_opponent_Goalkeeper=np.nan, y_player_opponent_8=np.nan,
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x_player_teammate_9=np.nan, x_player_teammate_10=np.nan,
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y_player_opponent_1=np.nan, y_player_opponent_2=np.nan, y_player_opponent_3=np.nan,
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y_player_teammate_1=np.nan, y_player_opponent_4=np.nan, y_player_opponent_5=np.nan,
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y_player_opponent_6=np.nan, y_player_teammate_2=np.nan, y_player_opponent_9=np.nan,
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y_player_opponent_10=np.nan, y_player_opponent_11=np.nan, y_player_teammate_3=np.nan,
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y_player_teammate_4=np.nan, y_player_teammate_5=np.nan, y_player_teammate_6=np.nan,
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y_player_teammate_7=np.nan, y_player_teammate_8=np.nan, y_player_teammate_9=np.nan,
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y_player_teammate_10=np.nan, x_player_opponent_7=np.nan, y_player_opponent_7=np.nan,
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x_player_teammate_Goalkeeper=np.nan, y_player_teammate_Goalkeeper=np.nan, shot_kick_off=False):
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model = load('xgboost.joblib')
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X_new = pd.DataFrame(columns=['minute', 'position_name', 'shot_body_part_name', 'shot_technique_name',
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'shot_type_name', 'shot_first_time', 'shot_one_on_one',
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'shot_aerial_won', 'shot_deflected', 'shot_open_goal',
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'shot_follows_dribble', 'shot_redirect', 'x1', 'y1',
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'number_of_players_opponents', 'number_of_players_teammates',
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'angle', 'distance', 'x_player_opponent_Goalkeeper',
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'x_player_opponent_8', 'x_player_opponent_1', 'x_player_opponent_2',
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'x_player_opponent_3', 'x_player_teammate_1', 'x_player_opponent_4',
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'x_player_opponent_5', 'x_player_opponent_6', 'x_player_teammate_2',
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'x_player_opponent_9', 'x_player_opponent_10', 'x_player_opponent_11',
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'x_player_teammate_3', 'x_player_teammate_4', 'x_player_teammate_5',
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'x_player_teammate_6', 'x_player_teammate_7', 'x_player_teammate_8',
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'x_player_teammate_9', 'x_player_teammate_10',
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'y_player_opponent_Goalkeeper', 'y_player_opponent_8',
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'y_player_opponent_1', 'y_player_opponent_2', 'y_player_opponent_3',
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'y_player_teammate_1', 'y_player_opponent_4', 'y_player_opponent_5',
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'y_player_opponent_6', 'y_player_teammate_2', 'y_player_opponent_9',
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'y_player_opponent_10', 'y_player_opponent_11', 'y_player_teammate_3',
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'y_player_teammate_4', 'y_player_teammate_5', 'y_player_teammate_6',
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'y_player_teammate_7', 'y_player_teammate_8', 'y_player_teammate_9',
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'y_player_teammate_10', 'x_player_opponent_7', 'y_player_opponent_7',
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'x_player_teammate_Goalkeeper', 'y_player_teammate_Goalkeeper',
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'shot_kick_off'])
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model = load('xgboost.joblib')
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X_new.loc[len(X_new.index)] = [minute, position_name, shot_body_part_name, shot_technique_name,
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shot_type_name, shot_first_time, shot_one_on_one,
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shot_aerial_won, shot_deflected, shot_open_goal,
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shot_follows_dribble, shot_redirect, x1, y1,
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number_of_players_opponents, number_of_players_teammates,
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angle, distance, x_player_opponent_Goalkeeper,
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x_player_opponent_8, x_player_opponent_1, x_player_opponent_2,
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x_player_opponent_3, x_player_teammate_1, x_player_opponent_4,
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x_player_opponent_5, x_player_opponent_6, x_player_teammate_2,
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x_player_opponent_9, x_player_opponent_10, x_player_opponent_11,
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x_player_teammate_3, x_player_teammate_4, x_player_teammate_5,
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x_player_teammate_6, x_player_teammate_7, x_player_teammate_8,
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x_player_teammate_9, x_player_teammate_10,
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y_player_opponent_Goalkeeper, y_player_opponent_8,
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y_player_opponent_1, y_player_opponent_2, y_player_opponent_3,
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y_player_teammate_1, y_player_opponent_4, y_player_opponent_5,
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y_player_opponent_6, y_player_teammate_2, y_player_opponent_9,
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y_player_opponent_10, y_player_opponent_11, y_player_teammate_3,
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y_player_teammate_4, y_player_teammate_5, y_player_teammate_6,
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y_player_teammate_7, y_player_teammate_8, y_player_teammate_9,
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y_player_teammate_10, x_player_opponent_7, y_player_opponent_7,
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x_player_teammate_Goalkeeper, y_player_teammate_Goalkeeper,
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shot_kick_off]
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return model.predict_proba(X_new)[0][1].round(2)
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X_new = pd.DataFrame(columns=['minute', 'position_name', 'shot_body_part_name', 'shot_technique_name',
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'shot_type_name', 'shot_first_time', 'shot_one_on_one',
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'shot_aerial_won', 'shot_deflected', 'shot_open_goal',
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'shot_follows_dribble', 'shot_redirect', 'x1', 'y1',
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'number_of_players_opponents', 'number_of_players_teammates',
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'angle', 'distance', 'x_player_opponent_Goalkeeper',
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'x_player_opponent_8', 'x_player_opponent_1', 'x_player_opponent_2',
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'x_player_opponent_3', 'x_player_teammate_1', 'x_player_opponent_4',
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'x_player_opponent_5', 'x_player_opponent_6', 'x_player_teammate_2',
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'x_player_opponent_9', 'x_player_opponent_10', 'x_player_opponent_11',
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'x_player_teammate_3', 'x_player_teammate_4', 'x_player_teammate_5',
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'x_player_teammate_6', 'x_player_teammate_7', 'x_player_teammate_8',
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'x_player_teammate_9', 'x_player_teammate_10',
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'y_player_opponent_Goalkeeper', 'y_player_opponent_8',
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'y_player_opponent_1', 'y_player_opponent_2', 'y_player_opponent_3',
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'y_player_teammate_1', 'y_player_opponent_4', 'y_player_opponent_5',
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'y_player_opponent_6', 'y_player_teammate_2', 'y_player_opponent_9',
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'y_player_opponent_10', 'y_player_opponent_11', 'y_player_teammate_3',
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'y_player_teammate_4', 'y_player_teammate_5', 'y_player_teammate_6',
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'y_player_teammate_7', 'y_player_teammate_8', 'y_player_teammate_9',
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'y_player_teammate_10', 'x_player_opponent_7', 'y_player_opponent_7',
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'x_player_teammate_Goalkeeper', 'y_player_teammate_Goalkeeper',
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'shot_kick_off'])
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X_new.loc[len(X_new.index)] = [minute, position_name, shot_body_part_name, shot_technique_name,
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shot_type_name, shot_first_time, shot_one_on_one,
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shot_aerial_won, shot_deflected, shot_open_goal,
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shot_follows_dribble, shot_redirect, x1, y1,
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number_of_players_opponents, number_of_players_teammates,
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angle, distance, x_player_opponent_Goalkeeper,
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x_player_opponent_8, x_player_opponent_1, x_player_opponent_2,
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x_player_opponent_3, x_player_teammate_1, x_player_opponent_4,
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x_player_opponent_5, x_player_opponent_6, x_player_teammate_2,
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x_player_opponent_9, x_player_opponent_10, x_player_opponent_11,
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x_player_teammate_3, x_player_teammate_4, x_player_teammate_5,
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x_player_teammate_6, x_player_teammate_7, x_player_teammate_8,
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x_player_teammate_9, x_player_teammate_10,
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y_player_opponent_Goalkeeper, y_player_opponent_8,
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y_player_opponent_1, y_player_opponent_2, y_player_opponent_3,
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y_player_teammate_1, y_player_opponent_4, y_player_opponent_5,
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y_player_opponent_6, y_player_teammate_2, y_player_opponent_9,
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y_player_opponent_10, y_player_opponent_11, y_player_teammate_3,
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y_player_teammate_4, y_player_teammate_5, y_player_teammate_6,
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y_player_teammate_7, y_player_teammate_8, y_player_teammate_9,
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y_player_teammate_10, x_player_opponent_7, y_player_opponent_7,
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x_player_teammate_Goalkeeper, y_player_teammate_Goalkeeper,
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shot_kick_off]
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X_new[['position_name',
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'shot_technique_name',
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'shot_type_name',
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'number_of_players_opponents',
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'number_of_players_teammates',
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'shot_body_part_name']] = X_new[['position_name',
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'shot_technique_name',
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'shot_type_name',
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'number_of_players_opponents',
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'number_of_players_teammates',
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'shot_body_part_name']].astype('category')
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X_new['minute'] = X_new['minute'].astype(int)
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X_new[['shot_first_time',
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'shot_one_on_one',
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'shot_aerial_won',
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'shot_deflected',
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'shot_open_goal',
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'shot_follows_dribble',
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'shot_redirect',
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'shot_kick_off']] = X_new[['shot_first_time',
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'shot_one_on_one',
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'shot_aerial_won',
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'shot_deflected',
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'shot_open_goal',
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'shot_follows_dribble',
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'shot_redirect',
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'shot_kick_off']].astype(bool)
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return model.predict_proba(X_new)[0][1].round(3)
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#XgBoost_2
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