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| # Assignment 4: Linear Regression on Synthetic Data | |
| # Build a linear regression model to predict house prices | |
| import numpy as np | |
| from sklearn.linear_model import LinearRegression | |
| # Synthetic dataset | |
| X = np.array([[1, 2], [2, 4], [3, 6], [4, 8]]) # Features: size, rooms | |
| y = np.array([100, 200, 300, 400]) # Prices | |
| # Train model | |
| model = LinearRegression() | |
| model.fit(X, y) | |
| # Predict on new data | |
| new_data = np.array([5, 10]) # Error: Shape mismatch, should be [[5, 10]] | |
| print(f"Predicted price: {model.predict(new_data)}") |