Update app.py

app.py

@@ -1,6 +1,6 @@
 import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt
+
 from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LinearRegression
 from sklearn.metrics import mean_squared_error, r2_score
@@ -41,22 +41,5 @@ print(f"Training R²: {train_r2:.4f}")
 print(f"Test R²: {test_r2:.4f}")
 print(f"\nModel Equation: y = {model.coef_[0]:.4f}x + {model.intercept_:.4f}")
 
-# Create visualization
-plt.figure(figsize=(10, 6))
-
-# Plot training data
-plt.scatter(X_train, y_train, color='blue', alpha=0.5, label='Training Data')
-# Plot test data
-plt.scatter(X_test, y_test, color='green', alpha=0.5, label='Test Data')
-
-# Plot regression line
-X_line = np.linspace(0, 10, 100).reshape(-1, 1)
-y_line = model.predict(X_line)
-plt.plot(X_line, y_line, color='red', label='Regression Line')
-
-plt.xlabel('X')
-plt.ylabel('y')
-plt.title('Linear Regression: Training and Test Data with Regression Line')
-plt.legend()
-plt.grid(True, alpha=0.3)
-plt.show()
+
+