Update app.py

app.py

@@ -1,7 +1,9 @@
 import streamlit as st
 import pandas as pd
+import matplotlib.pyplot as plt
 from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, r2_score
 
 # Load the dataset
 @st.cache_data
@@ -35,13 +37,21 @@ if selected_features:
     # Prediction
     y_pred = model.predict(X_test)
 
+
+    # Plot
+    fig, ax = plt.subplots()
+    ax.scatter(y_test, y_pred, edgecolors=(0, 0, 0))
+    ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)
+    ax.set_xlabel('Actual')
+    ax.set_ylabel('Predicted')
+    ax.set_title('Actual vs Predicted')
+    st.pyplot(fig)
+
+    # Model performance
+    st.write("Mean Squared Error", mean_squared_error(y_test, y_pred))
+    st.write("R-squared Score", r2_score(y_test, y_pred))
     # Display results
     st.write("Selected Features", selected_features)
     st.write("Model Coefficients", model.coef_)
     st.write("Predictions", y_pred)
     st.write("Actual Values", y_test.values)
-
-    # Model performance
-    from sklearn.metrics import mean_squared_error, r2_score
-    st.write("Mean Squared Error", mean_squared_error(y_test, y_pred))
-    st.write("R-squared Score", r2_score(y_test, y_pred))