41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
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import pandas
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from sklearn.manifold import TSNE
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from sklearn.cluster import KMeans
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import matplotlib.pyplot as plt
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from sklearn.preprocessing import StandardScaler
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from matplotlib.colors import ListedColormap
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#Данные
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data = pandas.read_csv('clean_data.csv')
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#Приведение строчных значений к числовым
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#work
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factorized_data_work, unique_values_work = pandas.factorize(data['work'])
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data['work'] = factorized_data_work
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#gymtime
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factorized_data_gymtime, unique_values_gymtime = pandas.factorize(data['gymtime'])
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data['gymtime'] = factorized_data_gymtime
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#Отбор нужных столбцов
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x = data[['age', 'weight', 'work', 'phy_health', 'phy_ex' 'gymtime']]
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#Нормализация данных
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scaler = StandardScaler()
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x_norm = scaler.fit_transform(x)
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#Выбор количества кластеров
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n_clusters = 3
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#Кластеризация с использованием KMeans
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kmeans = KMeans(n_clusters=n_clusters, init='k-means++', random_state=42)
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target = kmeans.fit_predict(x_norm)
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#t-SNE для визуализации результатов и кластеризации
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tsne = TSNE(n_components=2, random_state=42)
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x_tsne = tsne.fit_transform(x_norm)
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# Вывод результатов
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plt.scatter(x_tsne[:, 0], x_tsne[:, 1], c=target, cmap=ListedColormap(['#8b00ff', '#ff294d', '#77dd77']))
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plt.title('t-SNE кластеризация')
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plt.savefig("t-SNE.png")
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