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案例：探究用户对物品类别的喜好细分降维 ​

数据如下：

  • order_products__prior.csv：订单与商品信息
    • 字段：order_id, product_id, add_to_cart_order, reordered
  • products.csv：商品信息
    • 字段：product_id, product_name, aisle_id, department_id
  • orders.csv：用户的订单信息
    • 字段：order_id,user_id,eval_set,order_number,….
  • aisles.csv：商品所属具体物品类别
    • 字段： aisle_id, aisle

1 需求 ​

2 分析 ​

  • 1.获取数据
  • 2.数据基本处理
    • 2.1 合并表格
    • 2.2 交叉表合并
    • 2.3 数据截取
  • 3.特征工程 — pca
  • 4.机器学习（k-means）
  • 5.模型评估
    • sklearn.metrics.silhouette_score(X, labels)
      • 计算所有样本的平均轮廓系数
      • X：特征值
      • labels：被聚类标记的目标值

3 完整代码 ​

python
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
  • 1.获取数据
python
order_product = pd.read_csv("./data/instacart/order_products__prior.csv")
products = pd.read_csv("./data/instacart/products.csv")
orders = pd.read_csv("./data/instacart/orders.csv")
aisles = pd.read_csv("./data/instacart/aisles.csv")
order_product = pd.read_csv("./data/instacart/order_products__prior.csv")
products = pd.read_csv("./data/instacart/products.csv")
orders = pd.read_csv("./data/instacart/orders.csv")
aisles = pd.read_csv("./data/instacart/aisles.csv")

  • 2.数据基本处理

    • 2.1 合并表格
    python
    # 2.1 合并表格
table1 = pd.merge(order_product, products, on=["product_id", "product_id"])
table2 = pd.merge(table1, orders, on=["order_id", "order_id"])
table = pd.merge(table2, aisles, on=["aisle_id", "aisle_id"])
    # 2.1 合并表格
table1 = pd.merge(order_product, products, on=["product_id", "product_id"])
table2 = pd.merge(table1, orders, on=["order_id", "order_id"])
table = pd.merge(table2, aisles, on=["aisle_id", "aisle_id"])
    • 2.2 交叉表合并
    py
    table = pd.crosstab(table["user_id"], table["aisle"])
    table = pd.crosstab(table["user_id"], table["aisle"])
    • 2.3 数据截取
    py
    table = table[:1000]
    table = table[:1000]

  • 3.特征工程 — pca

    py
    transfer = PCA(n_components=0.9)
data = transfer.fit_transform(table)
    transfer = PCA(n_components=0.9)
data = transfer.fit_transform(table)

  • 4.机器学习（k-means）

    python
    estimator = KMeans(n_clusters=8, random_state=22)
estimator.fit_predict(data)
    estimator = KMeans(n_clusters=8, random_state=22)
estimator.fit_predict(data)

  • 5.模型评估

    python
    silhouette_score(data, y_predict)
    silhouette_score(data, y_predict)