Confusion regarding classification accuracy calculation and result The total number of data points for which the following result is obtained = 1500. Out of which, I have 


1473 labelled as 0 and
the remaining 27 as 1 . 


As can be seen from the confusion matrix, out of 27 data points belonging to class 1, I got only 1 data point misclassified as 0  . So, I calculated the accuracy for individual classes and got  Accuracy for class labelled as 0 = 98.2% and for the other as 1.7333%. Is this calculation correct? I am not sure...I did get a pretty good classification for the class labelled as 1 so why the accuracy for it is low?
The individual class accuracies should have been 100% for class0 and around 98% for class1

Does one misclassification reduce the accuracy of class 1 by so much amount? This is the how I calculated the individual class accuracies in MAtlab.

cmMatrix  = 
1473    0
1       26

acc_class0  = 100*(cmMatrix(1,1))/1500;
acc_class1= 100*(cmMatrix(2,2))/1500;
You might want to take a look at the confusion matrix  wiki page.

Accuracy = (TP + TN) / (P + N) = (26 + 1473)/1500 = 99.9%

I guess, if you want the break down by the class, it would have been:
1473/1473
and
26/27, but you have 1500 as the denominator in both classes.

Even though I think conventionally most people report on the entire model rather than just on a particular class.