Neural network approach

app.py

@@ -3,7 +3,8 @@ import numpy as np
 import gradio as gr
 from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LogisticRegression
-from sklearn.metrics import accuracy_score
+from sklearn.neural_network import MLPClassifier
+from sklearn.metrics import accuracy_score, classification_report
 import matplotlib.pyplot as plt
 
 # Loading the dataset
@@ -17,24 +18,24 @@ y = df["good"]
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
 # Creating and fitting the logistic regression model
-model = LogisticRegression()
-model.fit(X_train, y_train)
+logreg_model = LogisticRegression()
+logreg_model.fit(X_train, y_train)
 
-# Function to make predictions and display them on a graph
-def predict_and_plot(dirty, wait, lastyear, usa):
-    # Making prediction for a single input
+# Creating and fitting the neural network model
+nn_model = MLPClassifier(random_state=42)
+nn_model.fit(X_train, y_train)
+
+# Function to make predictions and display them on a graph (Logistic Regression)
+def predict_and_plot_logreg(dirty, wait, lastyear, usa):
     input_data = np.array([[dirty, wait, lastyear, usa]])
-    predicted_value = model.predict(input_data)[0]
+    predicted_value = logreg_model.predict(input_data)[0]
 
-    # Predicting on test set for comparison
-    y_pred = model.predict(X_test)
+    y_pred = logreg_model.predict(X_test)
 
-    # Plotting Wait vs Good
     plt.figure(figsize=(12, 6))
-    
-    # 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(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')
@@ -42,9 +43,8 @@ def predict_and_plot(dirty, wait, lastyear, usa):
     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(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')
@@ -52,32 +52,87 @@ def predict_and_plot(dirty, wait, lastyear, usa):
     plt.legend()
     plt.grid(True)
 
-    # Adjust layout and save the plot
     plt.tight_layout()
-    plt.savefig('output_plot.png')
+    plt.savefig('output_plot_logreg.png')
     plt.close()
 
-    return predicted_value, 'output_plot.png'
+    return predicted_value, 'output_plot_logreg.png'
+
+# Function to make predictions and display them on a graph (Neural Network)
+def predict_and_plot_nn(dirty, wait, lastyear, usa):
+    input_data = np.array([[dirty, wait, lastyear, usa]])
+    predicted_value = nn_model.predict(input_data)[0]
+
+    y_pred = nn_model.predict(X_test)
+
+    plt.figure(figsize=(12, 6))
+
+    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.ylabel('Good')
+    plt.legend()
+    plt.grid(True)
 
-# Creating Gradio UI
+    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.ylabel('Good')
+    plt.legend()
+    plt.grid(True)
+
+    plt.tight_layout()
+    plt.savefig('output_plot_nn.png')
+    plt.close()
+
+    return predicted_value, 'output_plot_nn.png'
+
+# Creating Gradio UI with two tabs for Logistic Regression and Neural Network
 with gr.Blocks() as demo:
-    gr.Markdown("# Logistic Regression Prediction")
+    gr.Markdown("# Prediction Models")
+
+    with gr.Tab("Logistic Regression"):
+        gr.Markdown("### Logistic Regression Prediction")
+
+        with gr.Row():
+            dirty_slider = gr.Slider(minimum=0, maximum=6, step=0.01, label="Dirty")
+            wait_slider = gr.Slider(minimum=0, maximum=5.3, step=0.01, label="Wait")
+            lastyear_slider = gr.Slider(minimum=0, maximum=70, step=0.01, label="Last Year")
+            usa_slider = gr.Slider(minimum=0, maximum=1, step=0.01, label="USA")
+
+        predict_button_logreg = gr.Button("Predict")
+
+        predicted_value_output_logreg = gr.Textbox(label="Predicted Value (Logistic Regression)")
+        plot_output_logreg = gr.Image(label="Actual vs Predicted Graph (Logistic Regression)")
+
+        predict_button_logreg.click(
+            fn=predict_and_plot_logreg,
+            inputs=[dirty_slider, wait_slider, lastyear_slider, usa_slider],
+            outputs=[predicted_value_output_logreg, plot_output_logreg]
+        )
+
+    with gr.Tab("Neural Network"):
+        gr.Markdown("### Neural Network Prediction")
 
-    with gr.Row():
-        dirty_slider = gr.Slider(minimum=0, maximum=6, step=0.01, label="Dirty")
-        wait_slider = gr.Slider(minimum=0, maximum=5.3, step=0.01, label="Wait")
-        lastyear_slider = gr.Slider(minimum=0, maximum=70, step=0.01, label="Last Year")
-        usa_slider = gr.Slider(minimum=0, maximum=1, step=0.01, label="USA")
+        with gr.Row():
+            dirty_slider_nn = gr.Slider(minimum=0, maximum=6, step=0.01, label="Dirty")
+            wait_slider_nn = gr.Slider(minimum=0, maximum=5.3, step=0.01, label="Wait")
+            lastyear_slider_nn = gr.Slider(minimum=0, maximum=70, step=0.01, label="Last Year")
+            usa_slider_nn = gr.Slider(minimum=0, maximum=1, step=0.01, label="USA")
 
-    predict_button = gr.Button("Predict")
+        predict_button_nn = gr.Button("Predict")
 
-    predicted_value_output = gr.Textbox(label="Predicted Value")
-    plot_output = gr.Image(label="Actual vs Predicted Graph")
+        predicted_value_output_nn = gr.Textbox(label="Predicted Value (Neural Network)")
+        plot_output_nn = gr.Image(label="Actual vs Predicted Graph (Neural Network)")
 
-    predict_button.click(
-        fn=predict_and_plot,
-        inputs=[dirty_slider, wait_slider, lastyear_slider, usa_slider],
-        outputs=[predicted_value_output, plot_output]
-    )
+        predict_button_nn.click(
+            fn=predict_and_plot_nn,
+            inputs=[dirty_slider_nn, wait_slider_nn, lastyear_slider_nn, usa_slider_nn],
+            outputs=[predicted_value_output_nn, plot_output_nn]
+        )
 
 demo.launch()