Create app.py

app.py

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+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
+
+# Generate sample dataset
+np.random.seed(42)
+X = np.random.rand(100, 1) * 10
+y = 2 * X + 1 + np.random.randn(100, 1) * 2
+
+# Create DataFrame for better data handling
+df = pd.DataFrame({'X': X.flatten(), 'y': y.flatten()})
+
+# Split the data
+X_train, X_test, y_train, y_test = train_test_split(
+    df[['X']], df['y'], test_size=0.2, random_state=42
+)
+
+# Create and train the model
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# Make predictions
+y_train_pred = model.predict(X_train)
+y_test_pred = model.predict(X_test)
+
+# Calculate metrics
+train_mse = mean_squared_error(y_train, y_train_pred)
+test_mse = mean_squared_error(y_test, y_test_pred)
+train_r2 = r2_score(y_train, y_train_pred)
+test_r2 = r2_score(y_test, y_test_pred)
+
+# Print model performance metrics
+print("Model Performance Metrics:")
+print("-" * 50)
+print(f"Training MSE: {train_mse:.4f}")
+print(f"Test MSE: {test_mse:.4f}")
+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()