<jupyter_start><jupyter_text>Before Hyperparameter Tuning Model report<jupyter_code>x_train,x_test,y_train,y_test= train_test_split(x,y,test_size=0.3)
model=SVC()
model.fit(x_train,y_train)
pred = model.predict(x_test)
print(classification_report(y_test,pred))<jupyter_output>precision    recall  f1-score   support

           0       0.96      0.80      0.88        61
           1       0.90      0.98      0.94       110

    accuracy                           0.92       171
   macro avg       0.93      0.89      0.91       171
weighted avg       0.92      0.92      0.92       171<jupyter_text>After Hyperparameter Tuning Model report<jupyter_code>param_grid={
    'C':[0.1,1,10,100],
    'gamma':[1,0.1,0.01,0.001,0.0001],
    'gamma':['scale','auto'],
    'kernel':['linear']
}
grid = GridSearchCV(SVC(), param_grid, refit=True, verbose=3, n_jobs=-1)
grid.fit(x_train,y_train)
print(grid.best_params_)
grid_pred =grid.predict(x_test)
print(classification_report(y_test,grid_pred))<jupyter_output>{'C': 10, 'gamma': 'scale', 'kernel': 'linear'}
              precision    recall  f1-score   support

           0       0.92      0.92      0.92        61
           1       0.95      0.95      0.95       110

    accuracy                           0.94       171
   macro avg       0.94      0.94      0.94       171
weighted avg       0.94      0.94      0.94       171
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