32 lines
920 B
Python
32 lines
920 B
Python
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# Regression Linéaire Simple
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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('Salary_Data.csv')
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X = dataset.iloc[:, :-1].values
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y = dataset.iloc[:, -1].values
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# Diviser le dataset entre le Training set et le Test set
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from sklearn.model_selection import train_test_split
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1.0/3, random_state = 0)
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# Construction du modèle
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from sklearn.linear_model import LinearRegression
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regressor = LinearRegression()
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regressor.fit(X_train, y_train)
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# Faire de nouvelles prédictions
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y_pred = regressor.predict(X_test)
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regressor.predict(15)
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# Visualiser les résultats
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plt.scatter(X_test, y_test, color = 'red')
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plt.plot(X_train, regressor.predict(X_train), 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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