A data set is given to you about utilities fraud detection. You have built aclassifier model and achieved a performance score of 98.5%. Is this a goodmodel? If yes, justify. If not, what can you do about it?

 Achieving a 98.5% performance score in a fraud detection model may or may not be good, depending on factors like class imbalance and the cost of false positives/negatives.

   - Justification would require analyzing the precision, recall, and F1-score of the model, considering the specific goals of the fraud detection task.

   - To improve it, you might consider different algorithms, adjusting the decision threshold, or addressing class imbalance.