Graph update

app.py

@@ -29,26 +29,28 @@ def predict_and_plot(dirty, wait, lastyear, usa):
     # Predicting on test set for comparison
     y_pred = model.predict(X_test)
 
-    # Creating subplots for dirty and wait, showing predicted value
-    fig, axs = plt.subplots(1, 2, figsize=(12, 6))
+    # Plotting Wait vs Good
+    plt.figure(figsize=(12, 6))
     
-    # Plot dirty variable distribution with predicted value
-    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[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()
+    # Scatter plot for Wait vs Good
+    plt.subplot(1, 2, 1)
+    plt.scatter(X_test.iloc[:, 1], y_test, color='blue', alpha=0.6, label='Actual Good Values')  # X_test[:, 1] for wait
+    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)
+
+    # Plotting USA vs Good
+    plt.subplot(1, 2, 2)
+    plt.scatter(X_test.iloc[:, 3], y_test, color='blue', alpha=0.6, label='Actual Good Values')  # X_test[:, 3] for usa
+    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.ylabel('Good')
+    plt.legend()
+    plt.grid(True)
 
     # Adjust layout and save the plot
     plt.tight_layout()