51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
# Regression Logistique
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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('Social_Network_Ads.csv')
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X = dataset.iloc[:, [2, 3]].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 = 0.25, random_state = 0)
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# Feature Scaling
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from sklearn.preprocessing import StandardScaler
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sc = StandardScaler()
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X_train = sc.fit_transform(X_train)
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X_test = sc.transform(X_test)
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# Construction du modèle
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from sklearn.linear_model import LogisticRegression
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classifier = LogisticRegression(random_state = 0)
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classifier.fit(X_train, y_train)
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# Faire de nouvelles prédictions
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y_pred = classifier.predict(X_test)
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# Matrice de confusion
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from sklearn.metrics import confusion_matrix
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cm = confusion_matrix(y_test, y_pred)
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# Visualiser les résultats
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from matplotlib.colors import ListedColormap
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X_set, y_set = X_train, y_train
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X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
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np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
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plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
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alpha = 0.4, cmap = ListedColormap(('red', 'green')))
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plt.xlim(X1.min(), X1.max())
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plt.ylim(X2.min(), X2.max())
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for i, j in enumerate(np.unique(y_set)):
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plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
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c = ListedColormap(('red', 'green'))(i), label = j)
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plt.title('Résultats du Training set')
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plt.xlabel('Age')
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plt.ylabel('Salaire Estimé')
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plt.legend()
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plt.show() |