Update README.md

README.md

@@ -67,8 +67,11 @@ Evaluation was performed on the overall validation dataset. Further analysis cou
 
 The primary evaluation metric used is Accuracy. A confusion matrix was also generated to visualize per-class performance.
 
-* **Accuracy:** The proportion of correctly classified images out of the total number of images evaluated.
-    $$\text{Accuracy} = \frac{\text{Number of correct predictions}}{\text{Total number of predictions}}$$
+* **Accuracy:** The proportion of correctly classified images out of the total number of images evaluated.  
+  $$
+  \text{Accuracy} = \frac{\text{Number of correct predictions}}{\text{Total number of predictions}}
+  $$
+
 
 * **Confusion Matrix:** A table that visualizes the performance of a classification model. Each row represents the instances in an actual class, while each column represents the instances in a predicted class.