1. Clusters

In [ ]:

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs

In [ ]:

X, y = make_blobs(n_samples=100, centers=3, random_state=10)

In [ ]:

X = pd.DataFrame(X)
X

Out[ ]:

	0	1
0	-2.324963	-6.699996
1	0.518568	-4.900868
2	2.443018	3.846526
3	5.826623	-9.922593
4	2.033002	5.289908
...	...	...
95	-0.736931	-4.957402
96	2.790440	3.068621
97	4.486980	-10.074298
98	6.268638	-9.381380
99	5.400508	-9.295867

100 rows × 2 columns

In [ ]:

Out[ ]:

array([2, 2, 1, 0, 1, 1, 0, 2, 1, 0, 0, 1, 1, 2, 2, 1, 0, 1, 0, 1, 0, 2,
       1, 2, 0, 1, 1, 1, 1, 0, 2, 1, 1, 0, 2, 2, 2, 1, 1, 1, 2, 0, 2, 2,
       1, 0, 0, 0, 2, 0, 1, 2, 0, 0, 2, 0, 1, 2, 0, 0, 1, 1, 2, 2, 2, 0,
       0, 2, 2, 2, 1, 0, 1, 1, 2, 1, 1, 2, 0, 0, 0, 1, 0, 1, 2, 1, 2, 0,
       2, 2, 0, 0, 0, 2, 2, 2, 1, 0, 0, 0])

In [ ]:

sns.scatterplot(x=X[0], y=X[1], hue=y)

Out[ ]:

<Axes: xlabel='0', ylabel='1'>

In [ ]:

from sklearn.cluster import KMeans

In [ ]:

km = KMeans(n_clusters=3)

In [ ]:

km.fit(X)

In [ ]:

pred = km.predict(X)

In [ ]:

sns.scatterplot(x=X[0], y=X[1], hue=pred)

Out[ ]:

<Axes: xlabel='0', ylabel='1'>

In [ ]:

km = KMeans(n_clusters=4)

In [ ]:

km.fit(X)
pred = km.predict(X)

In [ ]:

sns.scatterplot(x=X[0], y=X[1], hue=pred)

Out[ ]:

<Axes: xlabel='0', ylabel='1'>

In [ ]:

# 평가값, 하나의 ㅡㄹ러스터 안에 중심점으로부터 각각의 데이터 거리를 합한값
km.inertia_

Out[ ]:

154.07856318909234

In [ ]:

inertia_list = []

for i in range(2, 11):
    km = KMeans(n_clusters=i)
    km.fit(X)
    inertia_list.append(km.inertia_)

/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(

In [ ]:

inertia_list

Out[ ]:

[976.8773336900748,
 186.3658862010144,
 154.07856318909234,
 130.46261058161912,
 112.75070923501222,
 97.26002092022821,
 85.32225080499938,
 73.44112432158697,
 64.85517121205642]

In [ ]:

sns.lineplot(x=range(2, 11), y=inertia_list)
# 퍼짐 정도를 다합친값이 크면 너무 멀리 퍼졌다는 것이며 점점 너무 작아지면 가까운 것끼리 분류는 한것이므로 꺽이는 부분이 적절하다 판단 할 수 있다.

Out[ ]:

<Axes: >

2. marketing 데이터셋 살펴보기

In [ ]:

mkt_df = pd.read_csv('/content/drive/MyDrive/KDT/4. 머신러닝과 딥러닝/marketing.csv')

In [ ]:

mkt_df

Out[ ]:

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	Complain
0	5524	1957	Graduation	Single	58138.0	0	0	04-09-2012	58	635	88	546	172	88	88	3	8	10	4	7	0
1	2174	1954	Graduation	Single	46344.0	1	1	08-03-2014	38	11	1	6	2	1	6	2	1	1	2	5	0
2	4141	1965	Graduation	Together	71613.0	0	0	21-08-2013	26	426	49	127	111	21	42	1	8	2	10	4	0
3	6182	1984	Graduation	Together	26646.0	1	0	10-02-2014	26	11	4	20	10	3	5	2	2	0	4	6	0
4	5324	1981	PhD	Married	58293.0	1	0	19-01-2014	94	173	43	118	46	27	15	5	5	3	6	5	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2235	10870	1967	Graduation	Married	61223.0	0	1	13-06-2013	46	709	43	182	42	118	247	2	9	3	4	5	0
2236	4001	1946	PhD	Together	64014.0	2	1	10-06-2014	56	406	0	30	0	0	8	7	8	2	5	7	0
2237	7270	1981	Graduation	Divorced	56981.0	0	0	25-01-2014	91	908	48	217	32	12	24	1	2	3	13	6	0
2238	8235	1956	Master	Together	69245.0	0	1	24-01-2014	8	428	30	214	80	30	61	2	6	5	10	3	0
2239	9405	1954	PhD	Married	52869.0	1	1	15-10-2012	40	84	3	61	2	1	21	3	3	1	4	7	0

2240 rows × 21 columns

In [ ]:

pd.set_option('display.max_columns', 40)

In [ ]:

mkt_df.head()

Out[ ]:

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth
0	5524	1957	Graduation	Single	58138.0	0	0	04-09-2012	58	635	88	546	172	88	88	3	8	10	4	7
1	2174	1954	Graduation	Single	46344.0	1	1	08-03-2014	38	11	1	6	2	1	6	2	1	1	2	5
2	4141	1965	Graduation	Together	71613.0	0	0	21-08-2013	26	426	49	127	111	21	42	1	8	2	10	4
3	6182	1984	Graduation	Together	26646.0	1	0	10-02-2014	26	11	4	20	10	3	5	2	2	0	4	6
4	5324	1981	PhD	Married	58293.0	1	0	19-01-2014	94	173	43	118	46	27	15	5	5	3	6	5

In [ ]:

mkt_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2240 entries, 0 to 2239
Data columns (total 21 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   ID                   2240 non-null   int64  
 1   Year_Birth           2240 non-null   int64  
 2   Education            2240 non-null   object 
 3   Marital_Status       2240 non-null   object 
 4   Income               2216 non-null   float64
 5   Kidhome              2240 non-null   int64  
 6   Teenhome             2240 non-null   int64  
 7   Dt_Customer          2240 non-null   object 
 8   Recency              2240 non-null   int64  
 9   MntWines             2240 non-null   int64  
 10  MntFruits            2240 non-null   int64  
 11  MntMeatProducts      2240 non-null   int64  
 12  MntFishProducts      2240 non-null   int64  
 13  MntSweetProducts     2240 non-null   int64  
 14  MntGoldProds         2240 non-null   int64  
 15  NumDealsPurchases    2240 non-null   int64  
 16  NumWebPurchases      2240 non-null   int64  
 17  NumCatalogPurchases  2240 non-null   int64  
 18  NumStorePurchases    2240 non-null   int64  
 19  NumWebVisitsMonth    2240 non-null   int64  
 20  Complain             2240 non-null   int64  
dtypes: float64(1), int64(17), object(3)
memory usage: 367.6+ KB

(영어)	(한글)
ID	고객 아이디
Year_Birth	출생 연도
Education	학력
Marital_Status	결혼 여부
Income	소득
Kidhome	어린이 수
Teenhome	청소년 수
Dt_Customer	고객 등록일
Recency	마지막 구매일로부터 경과일
MntWines	와인 구매액
MntFruits	과일 구매액
MntMeatProducts	육류 제품 구매액
MntFishProducts	어류 제품 구매액
MntSweetProducts	단맛 제품 구매액
MntGoldProds	골드 제품 구매액
NumDealsPurchases	할인 행사 구매 수
NumWebPurchases	웹에서의 구매 수
NumCatalogPurchases	카탈로그에서의 구매 수
NumStorePurchases	매장에서의 구매 수
NumWebVisitsMonth	월별 웹 방문 수
Complain	불만 여부

In [ ]:

mkt_df.drop('ID', axis=1, inplace=True)

In [ ]:

mkt_df.describe()

Out[ ]:

	Year_Birth	Income	Kidhome	Teenhome	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	Complain
count	2240.000000	2216.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000
mean	1968.805804	52247.251354	0.444196	0.506250	49.109375	303.935714	26.302232	166.950000	37.525446	27.062946	44.021875	2.325000	4.084821	2.662054	5.790179	5.316518	0.009375
std	11.984069	25173.076661	0.538398	0.544538	28.962453	336.597393	39.773434	225.715373	54.628979	41.280498	52.167439	1.932238	2.778714	2.923101	3.250958	2.426645	0.096391
min	1893.000000	1730.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	1959.000000	35303.000000	0.000000	0.000000	24.000000	23.750000	1.000000	16.000000	3.000000	1.000000	9.000000	1.000000	2.000000	0.000000	3.000000	3.000000	0.000000
50%	1970.000000	51381.500000	0.000000	0.000000	49.000000	173.500000	8.000000	67.000000	12.000000	8.000000	24.000000	2.000000	4.000000	2.000000	5.000000	6.000000	0.000000
75%	1977.000000	68522.000000	1.000000	1.000000	74.000000	504.250000	33.000000	232.000000	50.000000	33.000000	56.000000	3.000000	6.000000	4.000000	8.000000	7.000000	0.000000
max	1996.000000	666666.000000	2.000000	2.000000	99.000000	1493.000000	199.000000	1725.000000	259.000000	263.000000	362.000000	15.000000	27.000000	28.000000	13.000000	20.000000	1.000000

In [ ]:

mkt_df.sort_values('Year_Birth')

Out[ ]:

	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	Complain
239	1893	2n Cycle	Single	60182.0	0	1	17-05-2014	23	8	0	5	7	0	2	1	1	0	2	4	0
339	1899	PhD	Together	83532.0	0	0	26-09-2013	36	755	144	562	104	64	224	1	4	6	4	1	0
192	1900	2n Cycle	Divorced	36640.0	1	0	26-09-2013	99	15	6	8	7	4	25	1	2	1	2	5	1
1950	1940	PhD	Single	51141.0	0	0	08-07-2013	96	144	0	7	0	0	6	1	3	1	4	5	0
424	1941	PhD	Married	93027.0	0	0	13-04-2013	77	1285	42	716	55	0	21	0	7	10	5	2	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
747	1995	Graduation	Single	71163.0	0	0	09-03-2014	30	283	17	372	138	106	44	1	3	8	12	1	0
1850	1995	2n Cycle	Single	83257.0	0	0	18-09-2012	56	536	27	590	38	107	67	1	5	10	12	6	1
696	1995	Graduation	Single	34824.0	0	0	26-03-2014	65	4	2	11	2	0	4	1	1	0	2	6	0
1170	1996	Basic	Married	14421.0	0	0	17-02-2014	81	0	0	2	3	3	8	1	1	0	2	5	0
46	1996	2n Cycle	Married	7500.0	0	0	09-11-2012	24	3	18	14	15	22	50	3	3	1	3	9	0

2240 rows × 20 columns

In [ ]:

mkt_df = mkt_df[mkt_df['Year_Birth'] > 1900]

In [ ]:

mkt_df.describe()

Out[ ]:

	Year_Birth	Income	Kidhome	Teenhome	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	Complain
count	2237.000000	2213.000000	2237.000000	2237.000000	2237.000000	2237.000000	2237.000000	2237.000000	2237.000000	2237.000000	2237.000000	2237.000000	2237.000000	2237.000000	2237.000000	2237.000000	2237.000000
mean	1968.901654	52236.581563	0.444345	0.506482	49.104604	303.995530	26.270451	166.916853	37.523022	27.068842	43.968708	2.326777	4.087170	2.662494	5.794367	5.319177	0.008941
std	11.701917	25178.603047	0.538467	0.544593	28.956073	336.574382	39.715972	225.661158	54.639909	41.293949	52.054318	1.932923	2.779461	2.923456	3.250940	2.426386	0.094152
min	1940.000000	1730.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	1959.000000	35246.000000	0.000000	0.000000	24.000000	24.000000	1.000000	16.000000	3.000000	1.000000	9.000000	1.000000	2.000000	0.000000	3.000000	3.000000	0.000000
50%	1970.000000	51373.000000	0.000000	0.000000	49.000000	174.000000	8.000000	67.000000	12.000000	8.000000	24.000000	2.000000	4.000000	2.000000	5.000000	6.000000	0.000000
75%	1977.000000	68487.000000	1.000000	1.000000	74.000000	504.000000	33.000000	232.000000	50.000000	33.000000	56.000000	3.000000	6.000000	4.000000	8.000000	7.000000	0.000000
max	1996.000000	666666.000000	2.000000	2.000000	99.000000	1493.000000	199.000000	1725.000000	259.000000	263.000000	362.000000	15.000000	27.000000	28.000000	13.000000	20.000000	1.000000

In [ ]:

mkt_df.sort_values('Income', ascending=False)

Out[ ]:

	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	Complain
2233	1977	Graduation	Together	666666.0	1	0	02-06-2013	23	9	14	18	8	1	12	4	3	1	3	6	0
617	1976	PhD	Together	162397.0	1	1	03-06-2013	31	85	1	16	2	1	2	0	0	0	1	1	0
687	1982	PhD	Married	160803.0	0	0	04-08-2012	21	55	16	1622	17	3	4	15	0	28	1	0	0
1300	1971	Master	Together	157733.0	1	0	04-06-2013	37	39	1	9	2	0	8	0	1	0	1	1	0
164	1973	PhD	Married	157243.0	0	1	01-03-2014	98	20	2	1582	1	2	1	15	0	22	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2078	1971	Graduation	Married	NaN	1	1	03-03-2013	82	71	1	16	0	0	9	4	2	1	3	8	0
2079	1954	Master	Together	NaN	0	1	23-06-2013	83	161	0	22	0	0	24	2	4	1	4	6	0
2081	1955	Graduation	Single	NaN	0	1	18-10-2013	95	264	0	21	12	6	147	3	6	1	5	7	0
2084	1943	Master	Widow	NaN	0	0	30-10-2013	75	532	126	490	164	126	126	1	5	5	11	1	0
2228	1978	2n Cycle	Together	NaN	0	0	12-08-2012	53	32	2	1607	12	4	22	0	0	0	1	0	0

2237 rows × 20 columns

In [ ]:

# mkt_df = mkt_df[mkt_df['Income' < 20000]] # NaN이 저장되지 않음
mkt_df = mkt_df[mkt_df['Income'] != 666666]

In [ ]:

mkt_df.sort_values('Income', ascending=False)

Out[ ]:

	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	Complain
617	1976	PhD	Together	162397.0	1	1	03-06-2013	31	85	1	16	2	1	2	0	0	0	1	1	0
687	1982	PhD	Married	160803.0	0	0	04-08-2012	21	55	16	1622	17	3	4	15	0	28	1	0	0
1300	1971	Master	Together	157733.0	1	0	04-06-2013	37	39	1	9	2	0	8	0	1	0	1	1	0
164	1973	PhD	Married	157243.0	0	1	01-03-2014	98	20	2	1582	1	2	1	15	0	22	0	0	0
1653	1977	Graduation	Together	157146.0	0	0	29-04-2013	13	1	0	1725	2	1	1	0	0	28	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2078	1971	Graduation	Married	NaN	1	1	03-03-2013	82	71	1	16	0	0	9	4	2	1	3	8	0
2079	1954	Master	Together	NaN	0	1	23-06-2013	83	161	0	22	0	0	24	2	4	1	4	6	0
2081	1955	Graduation	Single	NaN	0	1	18-10-2013	95	264	0	21	12	6	147	3	6	1	5	7	0
2084	1943	Master	Widow	NaN	0	0	30-10-2013	75	532	126	490	164	126	126	1	5	5	11	1	0
2228	1978	2n Cycle	Together	NaN	0	0	12-08-2012	53	32	2	1607	12	4	22	0	0	0	1	0	0

2236 rows × 20 columns

In [ ]:

mkt_df = mkt_df.dropna()

In [ ]:

mkt_df.isna().mean()

Out[ ]:

Year_Birth             0.0
Education              0.0
Marital_Status         0.0
Income                 0.0
Kidhome                0.0
Teenhome               0.0
Dt_Customer            0.0
Recency                0.0
MntWines               0.0
MntFruits              0.0
MntMeatProducts        0.0
MntFishProducts        0.0
MntSweetProducts       0.0
MntGoldProds           0.0
NumDealsPurchases      0.0
NumWebPurchases        0.0
NumCatalogPurchases    0.0
NumStorePurchases      0.0
NumWebVisitsMonth      0.0
Complain               0.0
dtype: float64

In [ ]:

mkt_df['Dt_Customer'] = pd.to_datetime(mkt_df['Dt_Customer'])

<ipython-input-84-51c2edf49ec9>:1: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.
  mkt_df['Dt_Customer'] = pd.to_datetime(mkt_df['Dt_Customer'])
<ipython-input-84-51c2edf49ec9>:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  mkt_df['Dt_Customer'] = pd.to_datetime(mkt_df['Dt_Customer'])

In [ ]:

mkt_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2212 entries, 0 to 2239
Data columns (total 20 columns):
 #   Column               Non-Null Count  Dtype         
---  ------               --------------  -----         
 0   Year_Birth           2212 non-null   int64         
 1   Education            2212 non-null   object        
 2   Marital_Status       2212 non-null   object        
 3   Income               2212 non-null   float64       
 4   Kidhome              2212 non-null   int64         
 5   Teenhome             2212 non-null   int64         
 6   Dt_Customer          2212 non-null   datetime64[ns]
 7   Recency              2212 non-null   int64         
 8   MntWines             2212 non-null   int64         
 9   MntFruits            2212 non-null   int64         
 10  MntMeatProducts      2212 non-null   int64         
 11  MntFishProducts      2212 non-null   int64         
 12  MntSweetProducts     2212 non-null   int64         
 13  MntGoldProds         2212 non-null   int64         
 14  NumDealsPurchases    2212 non-null   int64         
 15  NumWebPurchases      2212 non-null   int64         
 16  NumCatalogPurchases  2212 non-null   int64         
 17  NumStorePurchases    2212 non-null   int64         
 18  NumWebVisitsMonth    2212 non-null   int64         
 19  Complain             2212 non-null   int64         
dtypes: datetime64[ns](1), float64(1), int64(16), object(2)
memory usage: 362.9+ KB

In [ ]:

# 마지막으로 가입된 사람을 기준으로 가입 날짜(달) 구하기
mkt_df['pass_month'] = (mkt_df['Dt_Customer'].max().year * 12 + mkt_df['Dt_Customer'].max().month) - (mkt_df['Dt_Customer'].dt.year * 12 + mkt_df['Dt_Customer'].dt.month)

<ipython-input-86-a658f77a331d>:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  mkt_df['pass_month'] = (mkt_df['Dt_Customer'].max().year * 12 + mkt_df['Dt_Customer'].max().month) - (mkt_df['Dt_Customer'].dt.year * 12 + mkt_df['Dt_Customer'].dt.month)

In [ ]:

mkt_df.head()

Out[ ]:

	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	pass_month
0	1957	Graduation	Single	58138.0	0	0	2012-04-09	58	635	88	546	172	88	88	3	8	10	4	7	32
1	1954	Graduation	Single	46344.0	1	1	2014-08-03	38	11	1	6	2	1	6	2	1	1	2	5	4
2	1965	Graduation	Together	71613.0	0	0	2013-08-21	26	426	49	127	111	21	42	1	8	2	10	4	16
3	1984	Graduation	Together	26646.0	1	0	2014-10-02	26	11	4	20	10	3	5	2	2	0	4	6	2
4	1981	PhD	Married	58293.0	1	0	2014-01-19	94	173	43	118	46	27	15	5	5	3	6	5	11

In [ ]:

mkt_df.drop('Dt_Customer', axis=1, inplace=True)

<ipython-input-88-36e03d238c53>:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  mkt_df.drop('Dt_Customer', axis=1, inplace=True)

In [ ]:

mkt_df.head()

Out[ ]:

	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	pass_month
0	1957	Graduation	Single	58138.0	0	0	58	635	88	546	172	88	88	3	8	10	4	7	32
1	1954	Graduation	Single	46344.0	1	1	38	11	1	6	2	1	6	2	1	1	2	5	4
2	1965	Graduation	Together	71613.0	0	0	26	426	49	127	111	21	42	1	8	2	10	4	16
3	1984	Graduation	Together	26646.0	1	0	26	11	4	20	10	3	5	2	2	0	4	6	2
4	1981	PhD	Married	58293.0	1	0	94	173	43	118	46	27	15	5	5	3	6	5	11

In [ ]:

# mkt_df['Total_mnt'] = 와인, 과일, 육류, 어류, 단맛, 골드
mkt_df['Total_mnt'] = mkt_df[['MntWines', 'MntFruits', 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts', 'MntGoldProds']].sum(axis=1)

In [ ]:

# mkt_df['Children'] = 아이, 십대
mkt_df['Children'] = mkt_df[['Kidhome', 'Teenhome']].sum(axis=1)

In [ ]:

mkt_df.head()

Out[ ]:

	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	pass_month	Total_mnt	Children
0	1957	Graduation	Single	58138.0	0	0	58	635	88	546	172	88	88	3	8	10	4	7	32	1617	0
1	1954	Graduation	Single	46344.0	1	1	38	11	1	6	2	1	6	2	1	1	2	5	4	27	2
2	1965	Graduation	Together	71613.0	0	0	26	426	49	127	111	21	42	1	8	2	10	4	16	776	0
3	1984	Graduation	Together	26646.0	1	0	26	11	4	20	10	3	5	2	2	0	4	6	2	53	1
4	1981	PhD	Married	58293.0	1	0	94	173	43	118	46	27	15	5	5	3	6	5	11	422	1

In [ ]:

mkt_df.drop(['Kidhome', 'Teenhome'], axis=1, inplace=True)

In [ ]:

mkt_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2212 entries, 0 to 2239
Data columns (total 20 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Year_Birth           2212 non-null   int64  
 1   Education            2212 non-null   object 
 2   Marital_Status       2212 non-null   object 
 3   Income               2212 non-null   float64
 4   Recency              2212 non-null   int64  
 5   MntWines             2212 non-null   int64  
 6   MntFruits            2212 non-null   int64  
 7   MntMeatProducts      2212 non-null   int64  
 8   MntFishProducts      2212 non-null   int64  
 9   MntSweetProducts     2212 non-null   int64  
 10  MntGoldProds         2212 non-null   int64  
 11  NumDealsPurchases    2212 non-null   int64  
 12  NumWebPurchases      2212 non-null   int64  
 13  NumCatalogPurchases  2212 non-null   int64  
 14  NumStorePurchases    2212 non-null   int64  
 15  NumWebVisitsMonth    2212 non-null   int64  
 16  Complain             2212 non-null   int64  
 17  pass_month           2212 non-null   int64  
 18  Total_mnt            2212 non-null   int64  
 19  Children             2212 non-null   int64  
dtypes: float64(1), int64(17), object(2)
memory usage: 362.9+ KB

In [ ]:

mkt_df['Education'].value_counts()

Out[ ]:

Graduation    1115
PhD            480
Master         365
2n Cycle       198
Basic           54
Name: Education, dtype: int64

In [ ]:

mkt_df['Marital_Status'].value_counts()

Out[ ]:

Married     857
Together    571
Single      470
Divorced    231
Widow        76
Alone         3
Absurd        2
YOLO          2
Name: Marital_Status, dtype: int64

In [ ]:

mkt_df['Marital_Status'] = mkt_df['Marital_Status'].replace({'Married':'Partner', 'Together':'Partner', 'Single':'Single', 'Divorced':'Single', 'Widow':'Single', 'Alone':'Single', 'Absurd':'Single', 'YOLO':'Single'})

In [ ]:

mkt_df['Marital_Status'].value_counts()

Out[ ]:

Partner    1428
Single      784
Name: Marital_Status, dtype: int64

In [ ]:

mkt_df = pd.get_dummies(mkt_df, columns=['Education', 'Marital_Status'])

In [ ]:

mkt_df.head()

Out[ ]:

	Year_Birth	Income	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	pass_month	Total_mnt	Children	Education_Graduation	Education_PhD	Marital_Status_Partner	Marital_Status_Single
0	1957	58138.0	58	635	88	546	172	88	88	3	8	10	4	7	32	1617	0	1	0	0	1
1	1954	46344.0	38	11	1	6	2	1	6	2	1	1	2	5	4	27	2	1	0	0	1
2	1965	71613.0	26	426	49	127	111	21	42	1	8	2	10	4	16	776	0	1	0	1	0
3	1984	26646.0	26	11	4	20	10	3	5	2	2	0	4	6	2	53	1	1	0	1	0
4	1981	58293.0	94	173	43	118	46	27	15	5	5	3	6	5	11	422	1	0	1	1	0

3. 스케일링(Scaling)

데이터를 특정한 스케일로 통일하는 것
데이터를 모델링하기 전에 거치는 것이 좋음
다차원의 값들을 비교 분석하기 쉽게 만들어주며, 자료의 오버플로우나 언더플로우를 방지하여 최적화 과정에서의 안정성 및 수렴 속도를 향상

3-1. 스케일링의 종류

StandardScaler: 평균과 표준편차를 사용
MinMaxScaler: 최대/최소값이 각각 1과 0이 되도록 스케일링
RobustScaler: 중앙값과 IQR사용. 아웃라이어의 영향을 최소화

In [ ]:

movie = {'naver':[2, 4, 6, 8, 10], 'netflix':[1, 2, 3, 4, 5]}

In [ ]:

movie = pd.DataFrame(movie)
movie

Out[ ]:

	naver	netflix
0	2	1
1	4	2
2	6	3
3	8	4
4	10	5

In [ ]:

from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler

In [ ]:

min_max_scaler = MinMaxScaler()

In [ ]:

min_max_movie = min_max_scaler.fit_transform(movie)

In [ ]:

pd.DataFrame(min_max_movie, columns=['Naver', 'NetFlix'])

Out[ ]:

	Naver	NetFlix
0	0.00	0.00
1	0.25	0.25
2	0.50	0.50
3	0.75	0.75
4	1.00	1.00

3-2. 스케일링 적용하기

In [ ]:

ss = StandardScaler()

In [ ]:

ss.fit_transform(mkt_df)

Out[ ]:

array([[-1.01835202,  0.28710487,  0.31035323, ..., -0.52643733,
        -1.34960312,  1.34960312],
       [-1.27478522, -0.26088203, -0.38081349, ..., -0.52643733,
        -1.34960312,  1.34960312],
       [-0.33453017,  0.9131964 , -0.79551352, ..., -0.52643733,
         0.74095857, -0.74095857],
       ...,
       [ 1.03311355,  0.23334696,  1.45077832, ..., -0.52643733,
        -1.34960312,  1.34960312],
       [-1.10382975,  0.80317156, -1.41756357, ..., -0.52643733,
         0.74095857, -0.74095857],
       [-1.27478522,  0.04229031, -0.31169682, ...,  1.89956135,
         0.74095857, -0.74095857]])

In [ ]:

pd.DataFrame(ss.fit_transform(mkt_df))

Out[ ]:

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24
0	-1.018352	0.287105	0.310353	0.977660	1.552041	1.690293	2.453472	1.483713	0.852576	0.351030	1.426865	2.503607	-0.555814	0.692181	-0.09552	1.936840	1.676245	-1.264598	-0.313547	-0.158187	0.991895	-0.444542	-0.526437	-1.349603	1.349603
1	-1.274785	-0.260882	-0.380813	-0.872618	-0.637461	-0.718230	-0.651004	-0.634019	-0.733642	-0.168701	-1.126420	-0.571340	-1.171160	-0.132545	-0.09552	-1.714136	-0.963297	1.404572	-0.313547	-0.158187	0.991895	-0.444542	-0.526437	-1.349603	1.349603
2	-0.334530	0.913196	-0.795514	0.357935	0.570540	-0.178542	1.339513	-0.147184	-0.037254	-0.688432	1.426865	-0.229679	1.290224	-0.544908	-0.09552	-0.149432	0.280110	-1.264598	-0.313547	-0.158187	0.991895	-0.444542	-0.526437	0.740959	-0.740959
3	1.289547	-1.176114	-0.795514	-0.872618	-0.561961	-0.655787	-0.504911	-0.585335	-0.752987	-0.168701	-0.761665	-0.913000	-0.555814	0.279818	-0.09552	-1.974920	-0.920135	0.069987	-0.313547	-0.158187	0.991895	-0.444542	-0.526437	0.740959	-0.740959
4	1.033114	0.294307	1.554453	-0.392257	0.419540	-0.218684	0.152508	-0.001133	-0.559545	1.390492	0.332600	0.111982	0.059532	-0.132545	-0.09552	-0.801392	-0.307562	0.069987	-0.313547	-0.158187	-1.008171	-0.444542	1.899561	0.740959	-0.740959
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2207	-0.163575	0.430444	-0.104347	1.197084	0.419540	0.066770	0.079461	2.213965	3.928291	-0.168701	1.791619	0.111982	-0.555814	-0.132545	-0.09552	0.111352	1.218061	0.069987	-0.313547	-0.158187	0.991895	-0.444542	-0.526437	0.740959	-0.740959
2208	-1.958607	0.560123	0.241237	0.298631	-0.662628	-0.611184	-0.687527	-0.658360	-0.694954	2.429954	1.426865	-0.229679	-0.248141	0.692181	-0.09552	-1.974920	-0.271040	2.739158	-0.313547	-0.158187	-1.008171	-0.444542	1.899561	0.740959	-0.740959
2209	1.033114	0.233347	1.450778	1.787156	0.545373	0.222878	-0.103155	-0.366260	-0.385448	-0.688432	-0.761665	0.111982	2.213242	0.279818	-0.09552	-0.801392	1.052052	-1.264598	-0.313547	-0.158187	0.991895	-0.444542	-0.526437	-1.349603	1.349603
2210	-1.103830	0.803172	-1.417564	0.363866	0.092373	0.209498	0.773403	0.071892	0.330284	-0.168701	0.697355	0.795303	1.290224	-0.957271	-0.09552	-0.801392	0.391336	0.069987	-0.313547	-0.158187	-1.008171	2.249505	-0.526437	0.740959	-0.740959
2211	-1.274785	0.042290	-0.311697	-0.656159	-0.587128	-0.472917	-0.651004	-0.634019	-0.443481	0.351030	-0.396910	-0.571340	-0.555814	0.692181	-0.09552	1.154488	-0.722584	1.404572	-0.313547	-0.158187	-1.008171	-0.444542	1.899561	0.740959	-0.740959

2212 rows × 25 columns

In [ ]:

ss_df = pd.DataFrame(ss.fit_transform(mkt_df), columns=mkt_df.columns)

In [ ]:

ss_df

Out[ ]:

	Year_Birth	Income	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	Complain	pass_month	Total_mnt	Children	Education_2n Cycle	Education_Basic	Education_Graduation	Education_Master	Education_PhD	Marital_Status_Partner	Marital_Status_Single
0	-1.018352	0.287105	0.310353	0.977660	1.552041	1.690293	2.453472	1.483713	0.852576	0.351030	1.426865	2.503607	-0.555814	0.692181	-0.09552	1.936840	1.676245	-1.264598	-0.313547	-0.158187	0.991895	-0.444542	-0.526437	-1.349603	1.349603
1	-1.274785	-0.260882	-0.380813	-0.872618	-0.637461	-0.718230	-0.651004	-0.634019	-0.733642	-0.168701	-1.126420	-0.571340	-1.171160	-0.132545	-0.09552	-1.714136	-0.963297	1.404572	-0.313547	-0.158187	0.991895	-0.444542	-0.526437	-1.349603	1.349603
2	-0.334530	0.913196	-0.795514	0.357935	0.570540	-0.178542	1.339513	-0.147184	-0.037254	-0.688432	1.426865	-0.229679	1.290224	-0.544908	-0.09552	-0.149432	0.280110	-1.264598	-0.313547	-0.158187	0.991895	-0.444542	-0.526437	0.740959	-0.740959
3	1.289547	-1.176114	-0.795514	-0.872618	-0.561961	-0.655787	-0.504911	-0.585335	-0.752987	-0.168701	-0.761665	-0.913000	-0.555814	0.279818	-0.09552	-1.974920	-0.920135	0.069987	-0.313547	-0.158187	0.991895	-0.444542	-0.526437	0.740959	-0.740959
4	1.033114	0.294307	1.554453	-0.392257	0.419540	-0.218684	0.152508	-0.001133	-0.559545	1.390492	0.332600	0.111982	0.059532	-0.132545	-0.09552	-0.801392	-0.307562	0.069987	-0.313547	-0.158187	-1.008171	-0.444542	1.899561	0.740959	-0.740959
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2207	-0.163575	0.430444	-0.104347	1.197084	0.419540	0.066770	0.079461	2.213965	3.928291	-0.168701	1.791619	0.111982	-0.555814	-0.132545	-0.09552	0.111352	1.218061	0.069987	-0.313547	-0.158187	0.991895	-0.444542	-0.526437	0.740959	-0.740959
2208	-1.958607	0.560123	0.241237	0.298631	-0.662628	-0.611184	-0.687527	-0.658360	-0.694954	2.429954	1.426865	-0.229679	-0.248141	0.692181	-0.09552	-1.974920	-0.271040	2.739158	-0.313547	-0.158187	-1.008171	-0.444542	1.899561	0.740959	-0.740959
2209	1.033114	0.233347	1.450778	1.787156	0.545373	0.222878	-0.103155	-0.366260	-0.385448	-0.688432	-0.761665	0.111982	2.213242	0.279818	-0.09552	-0.801392	1.052052	-1.264598	-0.313547	-0.158187	0.991895	-0.444542	-0.526437	-1.349603	1.349603
2210	-1.103830	0.803172	-1.417564	0.363866	0.092373	0.209498	0.773403	0.071892	0.330284	-0.168701	0.697355	0.795303	1.290224	-0.957271	-0.09552	-0.801392	0.391336	0.069987	-0.313547	-0.158187	-1.008171	2.249505	-0.526437	0.740959	-0.740959
2211	-1.274785	0.042290	-0.311697	-0.656159	-0.587128	-0.472917	-0.651004	-0.634019	-0.443481	0.351030	-0.396910	-0.571340	-0.555814	0.692181	-0.09552	1.154488	-0.722584	1.404572	-0.313547	-0.158187	-1.008171	-0.444542	1.899561	0.740959	-0.740959

2212 rows × 25 columns

In [ ]:

rs = RobustScaler()

In [ ]:

rs_df = pd.DataFrame(rs.fit_transform(mkt_df), columns=mkt_df.columns)

In [ ]:

rs_df

Out[ ]:

	Year_Birth	Income	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	Complain	pass_month	Total_mnt	Children	Education_2n Cycle	Education_Basic	Education_Graduation	Education_Master	Education_PhD	Marital_Status_Partner	Marital_Status_Single
0	-0.722222	0.203497	0.18	0.955301	2.580645	2.210405	3.404255	2.50000	1.351064	0.5	1.00	2.00	-0.2	0.25	0.0	1.363636	1.246170	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	1.0
1	-0.888889	-0.151172	-0.22	-0.341996	-0.225806	-0.286705	-0.212766	-0.21875	-0.393617	0.0	-0.75	-0.25	-0.6	-0.25	0.0	-1.181818	-0.377937	1.0	0.0	0.0	0.0	0.0	0.0	-1.0	1.0
2	-0.277778	0.608718	-0.46	0.520790	1.322581	0.272832	2.106383	0.40625	0.372340	-0.5	1.00	0.00	1.0	-0.50	0.0	-0.090909	0.387130	-1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	0.777778	-0.743531	-0.46	-0.341996	-0.129032	-0.221965	-0.042553	-0.15625	-0.414894	0.0	-0.50	-0.50	-0.2	0.00	0.0	-1.363636	-0.351379	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	0.611111	0.208159	0.90	-0.005198	1.129032	0.231214	0.723404	0.59375	-0.202128	1.5	0.25	0.25	0.2	-0.25	0.0	-0.545455	0.025536	0.0	0.0	0.0	-1.0	0.0	1.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2207	-0.166667	0.296270	-0.06	1.109148	1.129032	0.527168	0.638298	3.43750	4.734043	0.0	1.25	0.25	-0.2	-0.25	0.0	0.090909	0.964249	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2208	-1.333333	0.380201	0.14	0.479210	-0.258065	-0.175723	-0.255319	-0.25000	-0.351064	2.5	1.00	0.00	0.0	0.25	0.0	-1.363636	0.048008	2.0	0.0	0.0	-1.0	0.0	1.0	0.0	0.0
2209	0.611111	0.168704	0.84	1.522869	1.290323	0.689017	0.425532	0.12500	-0.010638	-0.5	-0.50	0.25	1.6	0.00	0.0	-0.545455	0.862104	-1.0	0.0	0.0	0.0	0.0	0.0	-1.0	1.0
2210	-0.777778	0.537507	-0.82	0.524948	0.709677	0.675145	1.446809	0.68750	0.776596	0.0	0.50	0.75	1.0	-0.75	0.0	-0.545455	0.455567	0.0	0.0	0.0	-1.0	1.0	0.0	0.0	0.0
2211	-0.888889	0.045048	-0.18	-0.190229	-0.161290	-0.032370	-0.212766	-0.21875	-0.074468	0.5	-0.25	-0.25	-0.2	0.25	0.0	0.818182	-0.229826	1.0	0.0	0.0	-1.0	0.0	1.0	0.0	0.0

2212 rows × 25 columns

In [ ]:

mm = MinMaxScaler()

In [ ]:

mm_df = pd.DataFrame(mm.fit_transform(mkt_df), columns=mkt_df.columns)

In [ ]:

mm_df

Out[ ]:

	Year_Birth	Income	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	Complain	pass_month	Total_mnt	Children	Education_2n Cycle	Education_Basic	Education_Graduation	Education_Master	Education_PhD	Marital_Status_Partner	Marital_Status_Single
0	0.303571	0.351086	0.585859	0.425318	0.442211	0.316522	0.664093	0.335878	0.274143	0.200000	0.296296	0.357143	0.307692	0.35	0.0	0.914286	0.639683	0.000000	0.0	0.0	1.0	0.0	0.0	0.0	1.0
1	0.250000	0.277680	0.383838	0.007368	0.005025	0.003478	0.007722	0.003817	0.018692	0.133333	0.037037	0.035714	0.153846	0.25	0.0	0.114286	0.008730	0.666667	0.0	0.0	1.0	0.0	0.0	0.0	1.0
2	0.446429	0.434956	0.262626	0.285332	0.246231	0.073623	0.428571	0.080153	0.130841	0.066667	0.296296	0.071429	0.769231	0.20	0.0	0.457143	0.305952	0.000000	0.0	0.0	1.0	0.0	0.0	1.0	0.0
3	0.785714	0.155079	0.262626	0.007368	0.020101	0.011594	0.038610	0.011450	0.015576	0.133333	0.074074	0.000000	0.307692	0.30	0.0	0.057143	0.019048	0.333333	0.0	0.0	1.0	0.0	0.0	1.0	0.0
4	0.732143	0.352051	0.949495	0.115874	0.216080	0.068406	0.177606	0.103053	0.046729	0.333333	0.185185	0.107143	0.461538	0.25	0.0	0.314286	0.165476	0.333333	0.0	0.0	0.0	0.0	1.0	1.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2207	0.482143	0.370288	0.464646	0.474883	0.216080	0.105507	0.162162	0.450382	0.769470	0.133333	0.333333	0.107143	0.307692	0.25	0.0	0.514286	0.530159	0.333333	0.0	0.0	1.0	0.0	0.0	1.0	0.0
2208	0.107143	0.387659	0.565657	0.271936	0.000000	0.017391	0.000000	0.000000	0.024922	0.466667	0.296296	0.071429	0.384615	0.35	0.0	0.057143	0.174206	1.000000	0.0	0.0	0.0	0.0	1.0	1.0	0.0
2209	0.732143	0.343885	0.919192	0.608171	0.241206	0.125797	0.123552	0.045802	0.074766	0.066667	0.074074	0.107143	1.000000	0.30	0.0	0.314286	0.490476	0.000000	0.0	0.0	1.0	0.0	0.0	0.0	1.0
2210	0.285714	0.420217	0.080808	0.286671	0.150754	0.124058	0.308880	0.114504	0.190031	0.133333	0.222222	0.178571	0.769231	0.15	0.0	0.314286	0.332540	0.333333	0.0	0.0	0.0	1.0	0.0	1.0	0.0
2211	0.250000	0.318292	0.404040	0.056263	0.015075	0.035362	0.007722	0.003817	0.065421	0.200000	0.111111	0.035714	0.307692	0.35	0.0	0.742857	0.066270	0.666667	0.0	0.0	0.0	0.0	1.0	1.0	0.0

2212 rows × 25 columns

4. KMeans

KMeans 클러스터링 알고리즘은 n개의 중심점을 찍은 후에, 이 중심점에서 각 점간의 거리의 합이 가장 최소화가 되는 중심점 n의 위치를 찾고, 이 중심점에서 가까운 점들을 중심점을 기준으로 묶는 알고리즘
중심점은 각 군집의 데이터의 평균값을 위치로 가지게 됨
클러스터의 수를 정해줘야 함
중심점을 측정할 때 처음에 랜덤으로 중심점의 위치를 찾기 때문에 잘못하면 중심점의 점간의 거리가 Global optimum에 수렴하여 잘못된 분류를 할 수 있음

In [ ]:

inertia_list = []

for i in range(2, 11):
    km = KMeans(n_clusters=i, random_state=10)
    km.fit(mm_df)
    inertia_list.append(km.inertia_)

/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(

In [ ]:

sns.lineplot(x=range(2, 11), y=inertia_list)

Out[ ]:

<Axes: >

5. 실루엣 스코어

각 군집 간의 얼마나 효율적으로 분리 돼있는지를 나타냄
다른 군집과의 거리는 떨어져 있고 동일 군집끼리의 데이터는 서로 가깝게 잘 뭉쳐 있다는 의미
실루엣 분석은 실루엣 계수를 기반으로 하는데, 실루엣 계수는 개별 데이터가 가지는 군집화 지표

In [ ]:

from sklearn.metrics import silhouette_score

In [ ]:

score = []
for i in range(2,11):
    km = KMeans(n_clusters=i,random_state=10)
    km.fit(mm_df)
    pred = km.predict(mm_df)
    score.append(silhouette_score(mm_df,pred))

/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(

In [ ]:

score

Out[ ]:

[0.27069493833875846,
 0.24864017540307504,
 0.2720374467213452,
 0.3169512485472221,
 0.2687842581624041,
 0.3023437795916352,
 0.32784071353619376,
 0.3103663650975524,
 0.32666800120305484]

In [ ]:

sns.lineplot(x=range(2, 11), y=score)

Out[ ]:

<Axes: >

In [ ]:

km = KMeans(n_clusters=8, random_state=10)

In [ ]:

km.fit(mm_df)

In [ ]:

pred = km.predict(mm_df)

In [ ]:

pred

Out[ ]:

array([2, 2, 7, ..., 2, 5, 3], dtype=int32)

In [ ]:

mkt_df['label'] = pred

In [ ]:

mkt_df

Out[ ]:

	Year_Birth	Income	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	Complain	pass_month	Total_mnt	Children	Education_2n Cycle	Education_Basic	Education_Graduation	Education_Master	Education_PhD	Marital_Status_Partner	Marital_Status_Single	label
0	1957	58138.0	58	635	88	546	172	88	88	3	8	10	4	7	0	32	1617	0	0	0	1	0	0	0	1	1
1	1954	46344.0	38	11	1	6	2	1	6	2	1	1	2	5	0	4	27	2	0	0	1	0	0	0	1	6
2	1965	71613.0	26	426	49	127	111	21	42	1	8	2	10	4	0	16	776	0	0	0	1	0	0	1	0	8
3	1984	26646.0	26	11	4	20	10	3	5	2	2	0	4	6	0	2	53	1	0	0	1	0	0	1	0	0
4	1981	58293.0	94	173	43	118	46	27	15	5	5	3	6	5	0	11	422	1	0	0	0	0	1	1	0	3
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2235	1967	61223.0	46	709	43	182	42	118	247	2	9	3	4	5	0	18	1341	1	0	0	1	0	0	1	0	8
2236	1946	64014.0	56	406	0	30	0	0	8	7	8	2	5	7	0	2	444	3	0	0	0	0	1	1	0	3
2237	1981	56981.0	91	908	48	217	32	12	24	1	2	3	13	6	0	11	1241	0	0	0	1	0	0	0	1	1
2238	1956	69245.0	8	428	30	214	80	30	61	2	6	5	10	3	0	11	843	1	0	0	0	1	0	1	0	5
2239	1954	52869.0	40	84	3	61	2	1	21	3	3	1	4	7	0	26	172	2	0	0	0	0	1	1	0	3

2212 rows × 26 columns

In [ ]:

mkt_df['label'].value_counts()

Out[ ]:

0    458
3    305
8    288
6    257
5    240
2    175
1    165
7    137
9    125
4     62
Name: label, dtype: int64

(Python) 파이토치로 구현한 선형회 (0)	2023.06.15
(Python) 파이토치 (0)	2023.06.15
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(Python) 랜덤 포레스트 (0)	2023.06.15
(Python) 서포트 벡터 머신 (0)	2023.06.14

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(Python) KMeans 본문

(Python) KMeans

1. Clusters

2. marketing 데이터셋 살펴보기

3. 스케일링(Scaling)

3-1. 스케일링의 종류

3-2. 스케일링 적용하기

4. KMeans

5. 실루엣 스코어

'파이썬 머신러닝, 딥러닝' 카테고리의 다른 글

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