Hi OlutokiJohn

Did you solve the issue or are you still looking for a solution for this.

I did run your code with a dummy dataset and it works fine

```
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV
# defining parameter range
param_grid = {'C': [0.1, 1, 10, 100, 1000],
'gamma': [1, 0.1, 0.01, 0.001, 0.0001],
'kernel': ['rbf']}
grid = GridSearchCV(SVC(), param_grid, refit=True, verbose=3)
# fitting the model for grid search
grid.fit(X_train, y_train)
clf = svm.SVC(C = 10, gamma = 1)
clf.fit(X_train,y_train)
```

And got the output as follows:

```
Fitting 5 folds for each of 25 candidates, totalling 125 fits
[CV 1/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.637 total time= 0.0s
[CV 2/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.637 total time= 0.0s
[CV 3/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.625 total time= 0.0s
[CV 4/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.633 total time= 0.0s
[CV 5/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.633 total time= 0.0s
[CV 1/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.637 total time= 0.0s
[CV 2/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.637 total time= 0.0s
[CV 3/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.625 total time= 0.0s
[CV 4/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.633 total time= 0.0s
...
```

So I guess it could be an issue with data or session.