readme fixed

README.md

@@ -1,3 +1,13 @@
+---
+tags:
+  - deep-learning
+  - lstm
+  - human-activity-recognition
+  - sensor-data
+license: mit
+library_name: keras
+---
+
 # Human Activity Recognition with LSTM
 
 ## Overview
@@ -11,29 +21,29 @@ The model is trained on the **UCI HAR Dataset**, a widely used benchmark dataset
 Below are the precision, recall, and F1-score for each activity class:
 
 
-         precision    recall  f1-score   support
+```plaintext
+              precision    recall  f1-score   support
 
- Class 0       0.92      0.98      0.95       496
- Class 1       0.95      0.91      0.93       471
- Class 2       0.98      0.95      0.96       420
- Class 3       0.92      0.94      0.93       491
- Class 4       0.94      0.93      0.94       532
- Class 5       1.00      0.99      1.00       537
+     Class 0       0.92      0.98      0.95       496
+     Class 1       0.95      0.91      0.93       471
+     Class 2       0.98      0.95      0.96       420
+     Class 3       0.92      0.94      0.93       491
+     Class 4       0.94      0.93      0.94       532
+     Class 5       1.00      0.99      1.00       537
 
-accuracy                           0.95      2947
+    accuracy                           0.95      2947
+   macro avg       0.95      0.95      0.95      2947
+weighted avg       0.95      0.95      0.95      2947
 
-macro avg 0.95 0.95 0.95 2947 weighted avg 0.95 0.95 0.95 2947
+ ```
 
 ### Confusion Matrix
 The confusion matrix below visualizes the model's performance in classifying different activities:
 
-![Confusion Matrix](https://huggingface.co/MicS2/Human-Activity-Recognition/Figure_1.png)
+![Confusion Matrix](https://huggingface.co/Mic52/Human-Activity-Recognition/resolve/main/Figure_1.png)
 
 ## Next Steps
 - Improve the model with **GRU & CNN architectures**.
 - Expand testing with **real-world sensor data**.
 - Fine-tune hyperparameters for better generalization.
 
----
-license: mit
----