Update app.py

app.py

@@ -184,8 +184,7 @@ st.sidebar.header('User Input Features')
 selected_feature = st.sidebar.selectbox('Select feature for visualization', df.columns)
 selected_target = st.sidebar.selectbox('Select target variable', df.columns)
 
-# Display the raw data if checkbox is selected
-if st.checkbox('Show raw data'):
+
     st.write(df)
 
 # Visualization of selected feature
@@ -201,17 +200,7 @@ ax.scatter(df[selected_feature], df[selected_target], alpha=0.3)
 ax.set_xlabel(selected_feature)
 ax.set_ylabel(selected_target)
 
-# Show regression line if selected
-show_regression = st.checkbox('Show Regression Line')
-if show_regression and selected_feature in df.columns and selected_target == 'MedHouseVal':
-    X_feature = df[[selected_feature]]
-    y = df[selected_target]
-    model_feature = LinearRegression()
-    model_feature.fit(X_feature, y)
-    line = model_feature.predict(X_feature)
-    ax.plot(df[selected_feature], line, color='red', linewidth=2)
 
-st.pyplot(fig)
 
 # Simple Linear Regression
 X_single = df[['MedInc']]
@@ -249,6 +238,18 @@ y_pred_multi = model_multi.predict(X_test_multi)
 
 r2_multi = r2_score(y_test_multi, y_pred_multi)
 
+# Show regression line if selected
+show_regression = st.checkbox('Show Regression Line')
+if show_regression and selected_feature in df.columns and selected_target == 'MedHouseVal':
+    X_feature = df[[selected_feature]]
+    y = df[selected_target]
+    model_feature = LinearRegression()
+    model_feature.fit(X_feature, y)
+    line = model_feature.predict(X_feature)
+    ax.plot(df[selected_feature], line, color='red', linewidth=2)
+
+st.pyplot(fig)
+
 # Add checkbox for multilinear regression plot
 show_multilinear_plot = st.checkbox('Show Multilinear Regression Plot')