Update stages/model_evaluation.py

stages/model_evaluation.py

@@ -1,5 +1,31 @@
-import streamlit as st
-
-def main():
-    st.title("Model Evaluation/Testing")
+import streamlit as st
+
+def main():
+    st.title("Step 8: Model Evaluation")
+
+    st.markdown("""
+        ### **Model Evaluation** :clipboard:
+
+        After training your model, it's time to **evaluate** how well it performs. This step is crucial to determine if your model can generalize well to unseen data.
+
+        **:scales: Why Evaluate the Model?**
+        - **Measure Accuracy**: Check how accurately your model makes predictions.
+        - **Avoid Overfitting**: Ensure your model performs well not only on training data but also on new, unseen data.
+
+        **Key Evaluation Metrics**:
+        - **Accuracy**: The percentage of correct predictions.
+        - **Precision**: How many of the predicted positive cases are actually positive.
+        - **Recall**: How many of the actual positive cases are correctly predicted.
+        - **F1 Score**: The balance between precision and recall.
+        - **Confusion Matrix**: A visual representation of true vs. predicted values.
+
+        **:key: Evaluation Flow**:
+        - If your **evaluation score is less than 90%**, it's time to go **back to Step 6 (Feature Engineering)**. This means the features might need improvement.
+        - If the score is still **below 90% after revisiting Step 6**, consider **changing the model**. Sometimes, a different algorithm or model might perform better.
+        - If your **score is greater than 90%**, congratulations! You can move forward to **model deployment**.
+
+        **:rocket: In Short**: Model evaluation helps you assess how well your model performs. Based on the evaluation score, you'll either refine your model or proceed to deployment.
+    """)
+
+    st.divider()
 main()