注：本案例為黑馬的課堂案例，上傳僅為方便查看

# 1.獲取數(shù)據(jù) # 2.數(shù)據(jù)基本處理 # 2.1 合并表格 # 2.2 交叉表合并 # 2.3 數(shù)據(jù)截取 # 3.特征工程 — pca # 4.機(jī)器學(xué)習(xí)（k-means） # 5.模型評(píng)估 import pandas as pd from sklearn.decomposition import PCA from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score # 1.獲取數(shù)據(jù) 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.數(shù)據(jù)基本處理 # 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_idproduct_idadd_to_cart_orderreorderedproduct_nameaisle_iddepartment_iduser_ideval_setorder_numberorder_doworder_hour_of_daydays_since_prior_orderaisle01234

2	33120	1	1	Organic Egg Whites	86	16	202279	prior	3	5	9	8.0	eggs
26	33120	5	0	Organic Egg Whites	86	16	153404	prior	2	0	16	7.0	eggs
120	33120	13	0	Organic Egg Whites	86	16	23750	prior	11	6	8	10.0	eggs
327	33120	5	1	Organic Egg Whites	86	16	58707	prior	21	6	9	8.0	eggs
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() aisleair fresheners candlesasian foodsbaby accessoriesbaby bath body carebaby food formulabakery dessertsbaking ingredientsbaking supplies decorbeautybeers coolers...spreadsteatofu meat alternativestortillas flat breadtrail mix snack mixtrash bags linersvitamins supplementswater seltzer sparkling waterwhite winesyogurtuser_id12345

0	0	0	0	0	0	0	0	0	0	...	1	0	0	0	0	0	0	0	0	1
0	3	0	0	0	0	2	0	0	0	...	3	1	1	0	0	0	0	2	0	42
0	0	0	0	0	0	0	0	0	0	...	4	1	0	0	0	0	0	2	0	0
0	0	0	0	0	0	0	0	0	0	...	0	0	0	1	0	0	0	1	0	0
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 數(shù)據(jù)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.機(jī)器學(xué)習(xí)（k-means） estimator = KMeans(n_clusters=5) y_pre = estimator.fit_predict(trans_data) # 5.模型評(píng)估 silhouette_score(trans_data, y_pre) 0.4472179873751538

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