40 lines
1.1 KiB
Python
40 lines
1.1 KiB
Python
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# Regression Polynomiale
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# Importer les librairies
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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# Importer le dataset
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dataset = pd.read_csv('Position_Salaries.csv')
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X = dataset.iloc[:, 1:2].values
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y = dataset.iloc[:, -1].values
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# Construction du modèle
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from sklearn.linear_model import LinearRegression
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from sklearn.preprocessing import PolynomialFeatures
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poly_reg = PolynomialFeatures(degree = 4)
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X_poly = poly_reg.fit_transform(X)
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regressor = LinearRegression()
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regressor.fit(X_poly, y)
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# Faire de nouvelles prédictions
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regressor.predict(15)
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# Visualiser les résultats
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plt.scatter(X, y, color = 'red')
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plt.plot(X, regressor.predict(X_poly), color = 'blue')
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plt.title('Salaire vs Experience')
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plt.xlabel('Experience')
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plt.ylabel('Salaire')
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plt.show()
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# Visualiser les résultats (courbe plus lisse)
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X_grid = np.arange(min(X), max(X), 0.1)
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X_grid = X_grid.reshape((len(X_grid), 1))
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plt.scatter(X, y, color = 'red')
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plt.plot(X_grid, regressor.predict(poly_reg.fit_transform(X_grid)), color = 'blue')
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plt.title('Salaire vs Experience')
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plt.xlabel('Experience')
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plt.ylabel('Salaire')
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plt.show()
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