What does the classification report interpret? Class 1 indicates abnormal data
Classification report gives a perspective of your model performance. The 1st row shows the scores for class 0. The column 'support' displays how many object of class 0 were in the test set. The 2nd row provides info on the model performance for class 1. 

Recall for each class is calculated as follows:
True Positives/(True Positives + False Negatives)
E.g., recall for class 1: 15/(15+27) = 0.56

Precision for each class is calculated as follows:
True Positives/(True Positives + False Positives)
E.g., precision for class 1: 15/(15+22) = 0.41

This particular classification report shows that the performance of the model is poor. Accuracy as a metric may be misleading. Although the model shows 99% accuracy, it hardly detects the object of class 1 (I suppose these are the object of interest, the abnormal data you want to detect). 

One of the reasons for that may be the imbalance of the data, i.e. there are many objects of class 0 and few samples of class 1. As a result, the classifier is overfitting to class 0 to minimize error during training. Consider reading about some techniques to overcome the imbalance problem.