changing variables

app.py

@@ -34,20 +34,22 @@ def predict_and_plot_logreg(dirty, wait, lastyear, usa):
 
     plt.figure(figsize=(12, 6))
 
+    # Dirty vs Good plot
     plt.subplot(1, 2, 1)
-    plt.scatter(X_test.iloc[:, 1], y_test, color='blue', alpha=0.6, label='Actual Good Values')
-    plt.scatter(wait, predicted_value, color='red', label=f'Input: Wait={wait}, Predicted Good={predicted_value:.2f}', zorder=5)
-    plt.title('Wait vs Good')
-    plt.xlabel('Wait')
+    plt.scatter(X_test.iloc[:, 0], y_test, color='blue', alpha=0.6, label='Actual Good Values')  # X_test.iloc[:, 0] corresponds to 'dirty'
+    plt.scatter(dirty, predicted_value, color='red', label=f'Input: Dirty={dirty}, Predicted Good={predicted_value:.2f}', zorder=5)
+    plt.title('Dirty vs Good')
+    plt.xlabel('Dirty')
     plt.ylabel('Good')
     plt.legend()
     plt.grid(True)
 
+    # Wait vs Good plot
     plt.subplot(1, 2, 2)
-    plt.scatter(X_test.iloc[:, 3], y_test, color='blue', alpha=0.6, label='Actual Good Values')
-    plt.scatter(usa, predicted_value, color='red', label=f'Input: USA={usa}, Predicted Good={predicted_value:.2f}', zorder=5)
-    plt.title('USA vs Good')
-    plt.xlabel('USA')
+    plt.scatter(X_test.iloc[:, 1], y_test, color='blue', alpha=0.6, label='Actual Good Values')
+    plt.scatter(wait, predicted_value, color='red', label=f'Input: Wait={wait}, Predicted Good={predicted_value:.2f}', zorder=5)
+    plt.title('Wait vs Good')
+    plt.xlabel('Wait')
     plt.ylabel('Good')
     plt.legend()
     plt.grid(True)
@@ -67,20 +69,22 @@ def predict_and_plot_nn(dirty, wait, lastyear, usa):
 
     plt.figure(figsize=(12, 6))
 
+    # Dirty vs Good plot
     plt.subplot(1, 2, 1)
-    plt.scatter(X_test.iloc[:, 1], y_test, color='blue', alpha=0.6, label='Actual Good Values')
-    plt.scatter(wait, predicted_value, color='red', label=f'Input: Wait={wait}, Predicted Good={predicted_value:.2f}', zorder=5)
-    plt.title('Wait vs Good (NN)')
-    plt.xlabel('Wait')
+    plt.scatter(X_test.iloc[:, 0], y_test, color='blue', alpha=0.6, label='Actual Good Values')  # X_test.iloc[:, 0] corresponds to 'dirty'
+    plt.scatter(dirty, predicted_value, color='red', label=f'Input: Dirty={dirty}, Predicted Good={predicted_value:.2f}', zorder=5)
+    plt.title('Dirty vs Good (NN)')
+    plt.xlabel('Dirty')
     plt.ylabel('Good')
     plt.legend()
     plt.grid(True)
 
+    # Wait vs Good plot
     plt.subplot(1, 2, 2)
-    plt.scatter(X_test.iloc[:, 3], y_test, color='blue', alpha=0.6, label='Actual Good Values')
-    plt.scatter(usa, predicted_value, color='red', label=f'Input: USA={usa}, Predicted Good={predicted_value:.2f}', zorder=5)
-    plt.title('USA vs Good (NN)')
-    plt.xlabel('USA')
+    plt.scatter(X_test.iloc[:, 1], y_test, color='blue', alpha=0.6, label='Actual Good Values')
+    plt.scatter(wait, predicted_value, color='red', label=f'Input: Wait={wait}, Predicted Good={predicted_value:.2f}', zorder=5)
+    plt.title('Wait vs Good (NN)')
+    plt.xlabel('Wait')
     plt.ylabel('Good')
     plt.legend()
     plt.grid(True)