作者:Peter编辑:Peter今天给大家介绍一个聚类和降维结合的项目,分为两块内容:直接使用原数据,经过数据预处理和编码后,基于原生的K-Means和PCA/T-SNE实现用户的聚类使用基于Transformer的预训练模型转换后的高维数据,再使用K-Means和PCA/T-SNE实现用户的聚类本文先介绍第一种方案的完整过程。1 项目导图整个项目的导图:2 导入库In [1]:import pandas as pd import numpy as np np.random.seed(42)import matplotlib.pyplot as pltimport matplotlib.cm as cmimport plotly.express as pximport plotly.graph_objects as goimport seaborn as snsimport shapfrom sklearn.cluster import KMeansfrom sklearn.preprocessing import PowerTransformer, OrdinalEncoder, OneHotEncoderfrom sklearn.compose import ColumnTransformerfrom sklearn.pipeline import Pipelinefrom sklearn.manifold import TSNEfrom sklearn.metrics import silhouette_score, silhouette_samples, accurac
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