Update app.py

app.py

@@ -190,25 +190,16 @@ if st.checkbox('Show raw data'):
 
 # Visualization of selected feature
 st.subheader(f'Distribution of {selected_feature}')
-plt.figure(figsize=(10, 6))
-plt.hist(df[selected_feature], bins=30, edgecolor='black')
-st.pyplot(plt)
+fig, ax = plt.subplots()
+ax.hist(df[selected_feature], bins=30, edgecolor='black')
+st.pyplot(fig)
 
 # Scatter plot of selected feature vs target
 st.subheader(f'Scatter plot of {selected_feature} vs {selected_target}')
-plt.figure(figsize=(10, 6))
-plt.scatter(df[selected_feature], df[selected_target], alpha=0.3)
-plt.xlabel(selected_feature)
-plt.ylabel(selected_target)
-st.pyplot(plt)
-
-# Prediction
-st.subheader('Predict Median House Value')
-
-# Input values for prediction
-input_values = {}
-for feature in X.columns:
-    input_values[feature] = st.number_input(f'Enter {feature}', value=float(df[feature].mean()))
+fig, ax = plt.subplots()
+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')
@@ -219,7 +210,7 @@ if show_regression and selected_feature in X.columns and selected_target == 'Med
     line = model_feature.predict(X_feature)
     ax.plot(df[selected_feature], line, color='red', linewidth=2)
 
-st.pyplot(residuals)
+st.pyplot(fig)
 
 # Prediction
 st.subheader('Predict Median House Value')
@@ -234,4 +225,3 @@ if st.button('Predict'):
     prediction = model.predict(input_data)
     st.write(f'Predicted Median House Value: {prediction[0]}')
 
-