Update app.py

app.py

@@ -386,6 +386,13 @@ elif app_mode == "Prediction":
   
   # Display the plot within the Streamlit app
   st.pyplot(fig)
+  
+  mse = metrics.mean_squared_error(y_test, y_pred)
+  r2_score = metrics.r2_score(y_test, y_pred)
+
+  st.write(f"Mean Squared Error: {mse}")
+  st.write(f"R-squared: {r2_score}")
+  st.write("------------------------------------")
 
 # - - - - - - - - - - - - - - DECISION TREE REGRESSOR
   st.subheader('Decision Tree Regressor')
@@ -472,11 +479,32 @@ elif app_mode == "Prediction":
                     0.8520, 1.1060, 0.1560, 0.1560, 0.2480, 0.2530, 0.1470, 0.1440, 0.2080]
   }
   
+  #Code for Best Models - PyCaret/MLFlow
+  #!pip install pycaret --quiet
+  #!pip install datasets --quiet
+  #!pip install mlflow --quiet
+  
+  # Load the dataset from PyCaret
+  #from pycaret.datasets import get_data
+  #from pycaret.regression import setup, compare_models
+  
+  # Load the 'diamond' dataset
+  #data = tech_df[cols]
+  
+  # Initialize setup
+  #s = setup(data, target='NetProfitMargin_ratio', transform_target=True, log_plots=True, experiment_name='test1')
+  
+  # Compare regression models
+  #best_model = compare_models()
+
+  
   df = pd.DataFrame(data)
   
   # Display DataFrame as a table
   st.table(df)
 
   # - - - - - - - - - - - - - 
+  st.subheader("Old Feature Importance")
+  st.image('features_importance.png')
   st.subheader('Feature Importance')
   st.image('newplot.png')