Error correction

app.py

@@ -29,44 +29,26 @@ def predict_and_plot(dirty, wait, lastyear, usa):
     # Predicting on test set for comparison
     y_pred = model.predict(X_test)
 
-    # Creating subplots for each variable and showing predicted value
-    fig, axs = plt.subplots(2, 2, figsize=(12, 10))
+    # Creating subplots for dirty and wait, showing predicted value
+    fig, axs = plt.subplots(1, 2, figsize=(12, 6))
     
     # Plot dirty variable distribution with predicted value
-    axs[0, 0].hist(X_test[:, 0], bins=30, color='gray', alpha=0.5, label='Dirty Distribution')
-    axs[0, 0].axvline(dirty, color='orange', linestyle='--', label='Input Value (Dirty)')
-    axs[0, 0].axvline(predicted_value, color='red', linestyle='-', label='Predicted Value')
-    axs[0, 0].set_title('Distribution of Dirty')
-    axs[0, 0].set_xlabel('Dirty')
-    axs[0, 0].set_ylabel('Frequency')
-    axs[0, 0].legend()
+    axs[0].hist(X_test.iloc[:, 0], bins=30, color='gray', alpha=0.5, label='Dirty Distribution')  # Use iloc for pandas DataFrame
+    axs[0].axvline(dirty, color='orange', linestyle='--', label='Input Value (Dirty)')
+    axs[0].axvline(predicted_value, color='red', linestyle='-', label='Predicted Value')
+    axs[0].set_title('Distribution of Dirty')
+    axs[0].set_xlabel('Dirty')
+    axs[0].set_ylabel('Frequency')
+    axs[0].legend()
 
     # Plot wait variable distribution with predicted value
-    axs[0, 1].hist(X_test[:, 1], bins=30, color='gray', alpha=0.5, label='Wait Distribution')
-    axs[0, 1].axvline(wait, color='orange', linestyle='--', label='Input Value (Wait)')
-    axs[0, 1].axvline(predicted_value, color='red', linestyle='-', label='Predicted Value')
-    axs[0, 1].set_title('Distribution of Wait')
-    axs[0, 1].set_xlabel('Wait')
-    axs[0, 1].set_ylabel('Frequency')
-    axs[0, 1].legend()
-
-    # Plot lastyear variable distribution with predicted value
-    axs[1, 0].hist(X_test[:, 2], bins=30, color='gray', alpha=0.5, label='Lastyear Distribution')
-    axs[1, 0].axvline(lastyear, color='orange', linestyle='--', label='Input Value (Lastyear)')
-    axs[1, 0].axvline(predicted_value, color='red', linestyle='-', label='Predicted Value')
-    axs[1, 0].set_title('Distribution of Lastyear')
-    axs[1, 0].set_xlabel('Lastyear')
-    axs[1, 0].set_ylabel('Frequency')
-    axs[1, 0].legend()
-
-    # Plot usa variable distribution with predicted value
-    axs[1, 1].hist(X_test[:, 3], bins=30, color='gray', alpha=0.5, label='USA Distribution')
-    axs[1, 1].axvline(usa, color='orange', linestyle='--', label='Input Value (USA)')
-    axs[1, 1].axvline(predicted_value, color='red', linestyle='-', label='Predicted Value')
-    axs[1, 1].set_title('Distribution of USA')
-    axs[1, 1].set_xlabel('USA')
-    axs[1, 1].set_ylabel('Frequency')
-    axs[1, 1].legend()
+    axs[1].hist(X_test.iloc[:, 1], bins=30, color='gray', alpha=0.5, label='Wait Distribution')  # Use iloc for pandas DataFrame
+    axs[1].axvline(wait, color='orange', linestyle='--', label='Input Value (Wait)')
+    axs[1].axvline(predicted_value, color='red', linestyle='-', label='Predicted Value')
+    axs[1].set_title('Distribution of Wait')
+    axs[1].set_xlabel('Wait')
+    axs[1].set_ylabel('Frequency')
+    axs[1].legend()
 
     # Adjust layout and save the plot
     plt.tight_layout()