diff --git a/project_report.md b/project_report.md
index c1bdf67..babb24d 100644
--- a/project_report.md
+++ b/project_report.md
@@ -132,9 +132,9 @@ To establish a performance baseline, a classical Extreme Gradient Boosting (XGBo
 
 | Metric / Model | Classical XGBoost |
 | --- | --- |
-| Accuracy |  |
-| AUC |  |
-| F1-Score |  |
+| Accuracy | 0.581 |
+| AUC | 0.562 |
+| F1-Score | 0.652 |
 
 ### 4.2.2 XGBoost with GroupKFold Validation
 
@@ -142,9 +142,9 @@ To address the challenge of inter-subject variability, the validation strategy w
 
 | Metric / Model | XGBoost (GroupKFold) |
 | --- | --- |
-| Accuracy |  |
-| AUC |  |
-| F1-Score |  |
+| Accuracy | 0.586 |
+| AUC | 0.573 |
+| F1-Score | 0.651 |
 
 ### 4.2.3 Hybrid XGBoost with Autoencoder
 
@@ -152,9 +152,9 @@ To improve feature quality, a hybrid approach was introduced by pre-training a d
 
 | Metric / Model | XGBoost + Autoencoder |
 | --- | --- |
-| Accuracy |  |
-| AUC |  |
-| F1-Score |  |
+| Accuracy | 0.589 |
+| AUC | 0.575 |
+| F1-Score | 0.650 |
 
 ### 4.2.4 Robust XGBoost with MAD Outlier Removal
 
@@ -162,9 +162,9 @@ Recognizing that physiological and AU data often contain sensor artifacts, a rob
 
 | Metric / Model | XGBoost + MAD |
 | --- | --- |
-| Accuracy |  |
-| AUC |  |
-| F1-Score |  |
+| Accuracy | 0.641 |
+| AUC | 0.610 |
+| F1-Score | 0.733 |
 
 ### 4.2.5 Combined Dataset of Action Units and EyeTracking
 
@@ -174,9 +174,9 @@ By applying performance-based subject splitting, we ensured that the training an
 
 | Metric / Model | Final Combined Model |
 | --- | --- |
-| Accuracy |  |
-| AUC |  |
-| F1-Score |  |
+| Accuracy | 0.659 |
+| AUC | 0.621 |
+| F1-Score | 0.715 |
 
 ### 4.2.6 Regularized XGBoost with Complexity Control
 
@@ -186,9 +186,9 @@ By penalizing large weights and promoting feature sparsity, the model is forced
 
 | Metric / Model | Regularized XGBoost |
 | --- | --- |
-| Accuracy |  |
-| AUC |  |
-| F1-Score |  |
+| Accuracy | 0.665 |
+| AUC | 0.646 |
+| F1-Score | 0.727 |
 
 ### 4.3 Isolation Forest
 To start with unsupervised learning techniques, `IsolationForest.ipynb`was created to research how well a simple ensemble classificator performs on the created dataset.