39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
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# K-Means
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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('Mall_Customers.csv')
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X = dataset.iloc[:, [3, 4]].values
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# Utiliser la méthode elbow pour trouver le nombre optimal de clusters
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from sklearn.cluster import KMeans
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wcss = []
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for i in range(1, 11):
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kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 0)
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kmeans.fit(X)
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wcss.append(kmeans.inertia_)
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plt.plot(range(1, 11), wcss)
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plt.title('La méthode Elbow')
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plt.xlabel('Nombre de clusters')
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plt.ylabel('WCSS')
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plt.show()
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# Construction du modèle
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from sklearn.cluster import KMeans
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kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 0)
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y_kmeans = kmeans.fit_predict(X)
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# Visualiser les résultats
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plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], c = 'red', label = 'Cluster 1')
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plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], c = 'blue', label = 'Cluster 2')
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plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], c = 'green', label = 'Cluster 3')
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plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], c = 'cyan', label = 'Cluster 4')
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plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], c = 'magenta', label = 'Cluster 5')
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plt.title('Clusters de clients')
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plt.xlabel('Salaire annuel')
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plt.ylabel('Spending Score')
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plt.legend()
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