고객 장바구니 분석 level_1

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유아용품과 가장 연관이 있는 제품군 알아보기¶

In [41]:

# https://www.kaggle.com/c/instacart-market-basket-analysis/data

In [1]:

from glob import glob
import pandas as pd

In [2]:

glob("*")

Out[2]:

['aisles.csv',
 'departments.csv',
 'orders.csv',
 'order_products__prior.csv',
 'order_products__train.csv',
 'products.csv',
 'sample_submission.csv',
 'Untitled.ipynb']

In [9]:

pd.read_csv('products.csv').head()

Out[9]:

	product_id	product_name	aisle_id	department_id
0	1	Chocolate Sandwich Cookies	61	19
1	2	All-Seasons Salt	104	13
2	3	Robust Golden Unsweetened Oolong Tea	94	7
3	4	Smart Ones Classic Favorites Mini Rigatoni Wit...	38	1
4	5	Green Chile Anytime Sauce	5	13

In [8]:

pd.read_csv('order_products__train.csv').head()

Out[8]:

	order_id	product_id	add_to_cart_order	reordered
0	1	49302	1	1
1	1	11109	2	1
2	1	10246	3	0
3	1	49683	4	0
4	1	43633	5	1

In [7]:

pd.read_csv('order_products__prior.csv').head()

Out[7]:

	order_id	product_id	add_to_cart_order	reordered
0	2	33120	1	1
1	2	28985	2	1
2	2	9327	3	0
3	2	45918	4	1
4	2	30035	5	0

In [5]:

pd.read_csv('orders.csv').head()

Out[5]:

	order_id	user_id	eval_set	order_number	order_dow	order_hour_of_day	days_since_prior_order
0	2539329	1	prior	1	2	8	NaN
1	2398795	1	prior	2	3	7	15.0
2	473747	1	prior	3	3	12	21.0
3	2254736	1	prior	4	4	7	29.0
4	431534	1	prior	5	4	15	28.0

In [4]:

pd.read_csv('departments.csv').head()

Out[4]:

	department_id	department
0	1	frozen
1	2	other
2	3	bakery
3	4	produce
4	5	alcohol

In [3]:

pd.read_csv('aisles.csv').head()

Out[3]:

	aisle_id	aisle
0	1	prepared soups salads
1	2	specialty cheeses
2	3	energy granola bars
3	4	instant foods
4	5	marinades meat preparation

In [19]:

dept = pd.read_csv('departments.csv')[['department_id', 'department']]
pdt = pd.read_csv('products.csv')[['product_id', 'department_id']]
dept_pdt = pd.merge(dept, pdt)[['product_id', 'department']]
dept_pdt.head()

Out[19]:

	product_id	department
0	4	frozen
1	8	frozen
2	12	frozen
3	18	frozen
4	30	frozen

In [20]:

d_1 = pd.read_csv('order_products__train.csv')[['order_id', 'product_id', 'reordered']]
d_2 = pd.read_csv('order_products__prior.csv')[['order_id', 'product_id', 'reordered']]
order_product = pd.concat([d_1, d_2])
order_product.head()

Out[20]:

	order_id	product_id	reordered
0	1	49302	1
1	1	11109	1
2	1	10246	0
3	1	49683	0
4	1	43633	1

In [21]:

combined = pd.merge(order_product, dept_pdt)[['order_id', 'department', 'reordered']]
combined.head()

Out[21]:

	order_id	department	reordered
0	1	dairy eggs	1
1	816049	dairy eggs	1
2	1242203	dairy eggs	1
3	1383349	dairy eggs	1
4	1787378	dairy eggs	0

In [11]:

user_order = pd.read_csv('orders.csv')[['user_id', 'order_id']]
user_order.head()

Out[11]:

	user_id	order_id
0	1	2539329
1	1	2398795
2	1	473747
3	1	2254736
4	1	431534

In [24]:

final = pd.merge(user_order, combined)[['user_id', 'department', 'reordered']]
final.head()

Out[24]:

	user_id	department	reordered
0	1	beverages	0
1	1	snacks	0
2	1	dairy eggs	0
3	1	household	0
4	1	snacks	0

In [28]:

pvt = final.pivot_table(index = 'user_id', columns = 'department', values = 'reordered')
pvt.head()

Out[28]:

department	alcohol	babies	bakery	beverages	breakfast	bulk	canned goods	dairy eggs	deli	dry goods pasta	...	household	international	meat seafood	missing	other	pantry	personal care	pets	produce	snacks
user_id
1	NaN	NaN	NaN	0.866667	0.75	NaN	NaN	0.647059	NaN	NaN	...	0.666667	NaN	NaN	NaN	NaN	0.000000	NaN	NaN	0.200000	0.840000
2	NaN	NaN	0.0	0.333333	0.00	NaN	0.0	0.612245	0.416667	NaN	...	NaN	0.333333	0.0	NaN	NaN	0.363636	0.0	NaN	0.581395	0.489362
3	NaN	NaN	NaN	0.333333	NaN	NaN	NaN	0.714286	0.500000	0.75	...	0.000000	NaN	NaN	NaN	NaN	0.500000	NaN	NaN	0.684211	0.444444
4	0.0	NaN	0.0	0.000000	NaN	NaN	0.0	NaN	0.000000	NaN	...	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.000000	0.000000
5	NaN	NaN	NaN	NaN	NaN	NaN	0.0	0.444444	0.000000	0.00	...	0.000000	0.500000	NaN	NaN	NaN	0.333333	NaN	NaN	0.478261	0.000000

5 rows × 21 columns

In [40]:

corr = pvt.corr()
corr.head()

Out[40]:

department	alcohol	babies	bakery	beverages	breakfast	bulk	canned goods	dairy eggs	deli	dry goods pasta	...	household	international	meat seafood	missing	other	pantry	personal care	pets	produce	snacks
department
alcohol	1.000000	0.160500	0.140734	0.219895	0.093450	0.213245	0.138580	0.164133	0.137878	0.133933	...	0.196268	0.113650	0.138170	0.026186	0.177361	0.148339	0.151248	0.148252	0.132936	0.180079
babies	0.160500	1.000000	0.264185	0.271661	0.253578	0.119915	0.282913	0.368916	0.258728	0.302911	...	0.258133	0.178657	0.237875	0.129178	0.115108	0.279944	0.214191	0.171488	0.358751	0.334850
bakery	0.140734	0.264185	1.000000	0.311523	0.314608	0.173006	0.318422	0.456866	0.353979	0.342322	...	0.251465	0.235531	0.315626	0.118171	0.129102	0.355871	0.216225	0.196993	0.435772	0.377820
beverages	0.219895	0.271661	0.311523	1.000000	0.294760	0.150307	0.293046	0.406673	0.301139	0.307865	...	0.301129	0.223017	0.279380	0.153245	0.157346	0.326473	0.257679	0.253459	0.393546	0.396824
breakfast	0.093450	0.253578	0.314608	0.294760	1.000000	0.182291	0.292565	0.422232	0.292080	0.318724	...	0.250348	0.205902	0.257178	0.139459	0.127807	0.330778	0.224265	0.185809	0.385949	0.386953

5 rows × 21 columns

In [39]:

corr.sort_values(by='babies', ascending=False)['babies']

Out[39]:

department
babies             1.000000
dairy eggs         0.368916
produce            0.358751
snacks             0.334850
frozen             0.311711
dry goods pasta    0.302911
canned goods       0.282913
pantry             0.279944
beverages          0.271661
bakery             0.264185
deli               0.258728
household          0.258133
breakfast          0.253578
meat seafood       0.237875
personal care      0.214191
international      0.178657
pets               0.171488
alcohol            0.160500
missing            0.129178
bulk               0.119915
other              0.115108
Name: babies, dtype: float64

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