聚类kmeans案例

注：本案例为黑马的课堂案例，上传仅为方便查看

# 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

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
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
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"])

table.shape

(32434489, 14)

table.head()

	order_id	product_id	add_to_cart_order	reordered	product_name	aisle_id	department_id	user_id	eval_set	order_number	order_dow	order_hour_of_day	days_since_prior_order	aisle
0	2	33120	1	1	Organic Egg Whites	86	16	202279	prior	3	5	9	8.0	eggs
1	26	33120	5	0	Organic Egg Whites	86	16	153404	prior	2	0	16	7.0	eggs
2	120	33120	13	0	Organic Egg Whites	86	16	23750	prior	11	6	8	10.0	eggs
3	327	33120	5	1	Organic Egg Whites	86	16	58707	prior	21	6	9	8.0	eggs
4	390	33120	28	1	Organic Egg Whites	86	16	166654	prior	48	0	12	9.0	eggs

# 2.2 交叉表合并
data = pd.crosstab(table["user_id"], table["aisle"])

data.head()

aisle	air fresheners candles	asian foods	baby accessories	baby bath body care	baby food formula	bakery desserts	baking ingredients	baking supplies decor	beauty	beers coolers	...	spreads	tea	tofu meat alternatives	tortillas flat bread	trail mix snack mix	trash bags liners	vitamins supplements	water seltzer sparkling water	white wines	yogurt
user_id
1	0	0	0	0	0	0	0	0	0	0	...	1	0	0	0	0	0	0	0	0	1
2	0	3	0	0	0	0	2	0	0	0	...	3	1	1	0	0	0	0	2	0	42
3	0	0	0	0	0	0	0	0	0	0	...	4	1	0	0	0	0	0	2	0	0
4	0	0	0	0	0	0	0	0	0	0	...	0	0	0	1	0	0	0	1	0	0
5	0	2	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	3

5 rows × 134 columns

data.shape

(206209, 134)

# 2.3 数据截取
new_data = data[:1000]

new_data.shape

(1000, 134)

# 3.特征工程 — pca
transfer = PCA(n_components=0.9)
trans_data = transfer.fit_transform(new_data)

trans_data.shape

(1000, 22)

trans_data

array([[-2.27452872e+01, -7.32942365e-01, -2.48945893e+00, ...,
        -4.78491473e+00, -3.10742945e+00, -2.45192316e+00],
       [ 5.28638801e+00, -3.00176267e+01, -1.11226906e+00, ...,
         9.24145693e+00, -3.11309382e+00,  2.20144174e+00],
       [-6.52593099e+00, -3.87333123e+00, -9.23859508e+00, ...,
        -1.33929081e+00,  1.25062993e+00,  6.12717485e-01],
       ...,
       [ 1.31226615e+01, -2.77296885e+01, -4.62403246e+00, ...,
         7.40793534e+00,  1.03829352e+00, -1.39058393e+01],
       [ 1.64905900e+02, -8.54916188e+01,  1.90577481e-02, ...,
        -5.62014943e+00, -1.38488891e+01, -7.11424774e+00],
       [-1.60244724e+00,  1.82037661e+00,  8.55756408e+00, ...,
         3.69860152e+00,  2.82248188e+00, -3.79491023e+00]])

# 4.机器学习（k-means）
estimator = KMeans(n_clusters=5)
y_pre = estimator.fit_predict(trans_data)

# 5.模型评估
silhouette_score(trans_data, y_pre)

0.4472179873751538