How can I improve the accuracy of my confusion matrix to 100%?
Bayes error


To answer your question, first I should explain Bayes error. Assuming we know the exact joint distribution of feature vectors ($\mathbf{x}$) and each class (${C_k}$) as 
$P(\mathbf{x},{C_k})$, we build a classifier which assigns label $k$ to each feature vector by this criteria $$\mathop {\arg \max }\limits_k P(\left. {{C_k}} \right|\mathbf{x})$$
It can be shown that this is the best possible classifier by calculating the expected classification error on the whole feature space. This expected classification error is called Bayes error and is the minimum achievable classification error for this feature-label space.


  Training error


If you evaluate your model on the training data and calculate confusion matrix using the training samples you may achieve 100% accuracy because your model may overfit your training data. It means your training error is 0 even the Bayes error may not be.


  Generalization error


If you evaluate your model on the test data and calculate confusion matrix using the test samples you can not achieve 100% accuracy because you are evaluating the generalization capability of your model and its error can not be less than Bayes error.