Update app.py

app.py

@@ -255,13 +255,10 @@ def bootstrap_stats(predicted_target, num_samples=1000):
         bootstrapped_means.append(np.mean(bootstrap_sample))
 
     # Calculate lower and higher bounds
-    lower_bound = np.percentile(bootstrapped_means, 5.)
-    higher_bound = np.percentile(bootstrapped_means, 95.)
+    lower_bound = np.percentile(bootstrapped_means, 25.)
+    higher_bound = np.percentile(bootstrapped_means, 75.)
 
-    # Calculate the mean value
-    mean_value = np.mean(bootstrapped_means)
-
-    return lower_bound, higher_bound, mean_value
+    return lower_bound, higher_bound
 
 # Apply KNN and get predicted Predicted_target values
 predicted_target = knn_predict(filtered_data, 'Predicted_target', ['latitude', 'longitude', 'area_feature'])
@@ -269,10 +266,12 @@ predicted_target = knn_predict(filtered_data, 'Predicted_target', ['latitude', '
 # Check if there are predictions to display
 if 'Predicted_target' in filtered_data.columns and not np.all(predicted_target == 0):
     # Add predicted Predicted_target values to filtered_data
-    filtered_data['Predicted_target'] = predicted_target
+    filtered_data['target_column'] = bound_data
 
     # Apply bootstrap on the predicted values
-    lower_bound, higher_bound, mean_value = bootstrap_stats(predicted_target)
+    lower_bound, higher_bound = bootstrap_stats(bound_data)
+
+    mean_value = np.mean(filtered_data['Predicted_target'])
 
     # Display the results with custom styling
     st.markdown("## **Resultado da Análise Estatística**")