country_name
overall_value
85 / 100
club_name
overall_value
80 / 100
attributes_df.drop(['height_cm', 'weight_kg', 'weak_foot', 'pace'], axis=1, inplace=True)
lr = LinearRegression()
lr.fit(X_train, y_train)
y_pred_lr = lr.predict(X_test)
svr_rbf=SVR(C=1.0, epsilon=0.2, kernel='rbf')
svr_rbf.fit(X_train, y_train)
y_pred_svr = svr_rbf.predict(X_test)
forest=RandomForestRegressor(n_estimators=20, max_depth=10, criterion='mse')
forest.fit(X_train,y_train)
y_pred_rfr = forest.predict(X_test)
evaluation(y_test, y_pred_lr, 'LinearRegression')
evaluation(y_test, y_pred_svr, 'SupportVectorRegressor')
evaluation(y_test, y_pred_rfr, 'RandomForestRegressor')
new_forest=RandomForestRegressor(n_estimators=20, max_depth=3, criterion='mse')
new_forest.fit(X_train,y_train)
new_y_pred_rfr = new_forest.predict(X_test)
evaluation(y_test, new_y_pred_rfr, 'RandomForestRegressor')