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| # imports | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from sklearn.linear_model import LinearRegression | |
| from sklearn.metrics import mean_squared_error, r2_score | |
| # generate random data-set | |
| np.random.seed(0) | |
| x = np.random.rand(100, 1) | |
| y = 2 + 3 * x + np.random.rand(100, 1) | |
| # sckit-learn implementation | |
| # Model initialization | |
| regression_model = LinearRegression() | |
| # Fit the data(train the model) | |
| regression_model.fit(x, y) | |
| # Predict | |
| y_predicted = regression_model.predict(x) | |
| # model evaluation | |
| rmse = mean_squared_error(y, y_predicted) | |
| r2 = r2_score(y, y_predicted) | |
| # printing values | |
| print('Slope:' ,regression_model.coef_) | |
| print('Intercept:', regression_model.intercept_) | |
| print('Root mean squared error: ', rmse) | |
| print('R2 score: ', r2) | |
| # plotting values | |
| # data points | |
| plt.scatter(x, y, s=10) | |
| plt.xlabel('x') | |
| plt.ylabel('y') | |
| # predicted values | |
| plt.plot(x, y_predicted, color='r') | |
| plt.show() |
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