explain

app.py

@@ -228,13 +228,14 @@ With no need for jargon, SSDS delivers tangible value to our fintech operations.
     =============
     ![summary](file=./xgb/data.png)
     
-    sorted feature from top(most import)
+    sorted feature from top(most importance)
     
     dist_subway when at low value(green) make big impact to price
     
     dist_store doesnt make much impact to price
     
     high age lower the price
+    
     low age raise the price
     
     
@@ -264,7 +265,11 @@ With no need for jargon, SSDS delivers tangible value to our fintech operations.
     over all trend is negative that mean, closer to subway is contribute to higher price
     
     there is a point at 6500 far from subway and it has negative impact on price, despite is is close to store(dist_stores)
+    
+    ![1st decision tree](file=./xgb/tree.svg)
     """)
+
+
     gr.DataFrame(df)
 
   with gr.Tab("Fine Tune LLM"):