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40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LinearRegression
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# Generate synthetic data
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np.random.seed(42)
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X = 2 * np.random.rand(100, 1)
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y = 4 + 3 * X + np.random.randn(100, 1)
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# Split the data into training and testing sets
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# Create a linear regression model
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lin_reg = LinearRegression()
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# Fit the model to the training data
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lin_reg.fit(X_train, y_train)
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# Get the slope (coef_) and the intercept (intercept_)
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slope = lin_reg.coef_
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intercept = lin_reg.intercept_
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# Print the slope and intercept
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print("Slope (Coefficient): ", slope)
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print("Intercept: ", intercept)
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# Predict y values for the test set
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y_pred = lin_reg.predict(X_test)
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# Visualize the training data and the regression line
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plt.scatter(X_train, y_train, color='blue', label='Training Data')
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plt.scatter(X_test, y_test, color='green', label='Testing Data')
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plt.plot(X_test, y_pred, color='red', linewidth=2, label='Regression Line')
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plt.xlabel('X')
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plt.ylabel('y')
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plt.title('Linear Regression Demonstration')
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plt.legend()
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plt.show()
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