Matrix Confusion - Get Model Precision
A confusion matrix gives you the following:

[TP, FP]
[FN, TN]


where TP = 'true positives'; FP = 'false positives'; FN = 'false negatives'; TN = 'true negatives'.

You can read more here: http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/

By taking TP+TN and dividing by TP+FP+FN+TN, you can get the classification accuracy of your model. In your case, that means (9779+148)/(9779+107+2227+148) = about 81%

More details:

This type of confusion matrix is used for binary classification. 


A 'true positive' in this case means a true instance of class 0; that is, the model predicts that a given example belongs to class 0 and it really does belong to class 0. 
A 'true negative' means a true instance of class 1.
A 'false positive' means the model predicts that the example belongs to class 0 when it really belongs to class 1;
A 'false negative' means the model predicts that the example belongs to class 1 when it really belongs to class 0.