Update app.py

app.py

@@ -77,8 +77,11 @@ def main():
         st.plotly_chart(fig2)
 
         st.subheader('Performance Metrics')
-       st.write('The metrics below provide a quantitative measure of the model’s accuracy: Mean Absolute Error (MAE): A lower value indicates better performance. Mean Squared Error (MSE): A lower value indicates better performance, and it penalizes larger errors more than MAE.- Root Mean Squared Error (RMSE): A lower value indicates better performance, similar to MSE, but in the same units as the target variable.
-        ')
+       st.write('The metrics below provide a quantitative measure of the model’s accuracy.')
+        st.write('**Mean Absolute Error (MAE)**: A lower value indicates better performance.')
+        st.write('**Mean Squared Error (MSE)**: A lower value indicates better performance, and it penalizes larger errors more than MAE.')
+        st.write('**Root Mean Squared Error (RMSE)**: A lower value indicates better performance, similar to MSE, but in the same units as the target variable.')
+        
         actual = df['y']
         predicted = forecast['yhat'][:len(df)]
         metrics = calculate_performance_metrics(actual, predicted)