1.Clusters(클러스터)¶
- 유사한 특성을 가진 개체들의 집합
- 고객 분류, 유전자 분석, 이미지 분할
In [1]:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
In [2]:
X, y = make_blobs(n_samples=100, centers=3, random_state= 2023)
In [3]:
X = pd.DataFrame(X)
X
Out[3]:
| 0 | 1 | |
|---|---|---|
| 0 | 1.183732 | -8.288026 |
| 1 | 1.092865 | -7.411113 |
| 2 | -5.746528 | 0.425609 |
| 3 | 0.305555 | -8.296799 |
| 4 | -7.085940 | -2.835521 |
| ... | ... | ... |
| 95 | -5.843954 | 7.807627 |
| 96 | 2.823700 | -7.205193 |
| 97 | 1.151901 | -8.539375 |
| 98 | -3.377065 | 9.044279 |
| 99 | -4.118107 | 8.735529 |
100 rows × 2 columns
In [4]:
y
Out[4]:
array([1, 1, 2, 1, 2, 1, 2, 2, 0, 1, 1, 2, 1, 1, 0, 0, 1, 2, 2, 0, 0, 1,
2, 0, 0, 2, 2, 0, 0, 0, 2, 2, 1, 2, 0, 0, 1, 2, 1, 0, 2, 0, 2, 1,
2, 0, 2, 1, 2, 0, 0, 2, 1, 1, 0, 2, 2, 2, 1, 1, 2, 0, 1, 1, 2, 1,
2, 0, 2, 2, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 2, 1, 0, 0, 0, 0, 2, 1,
2, 2, 1, 0, 0, 1, 2, 0, 1, 1, 0, 0])
In [5]:
sns.scatterplot(x=X[0],y=X[1],hue=y)
Out[5]:
<Axes: xlabel='0', ylabel='1'>
In [6]:
from sklearn.cluster import KMeans
In [7]:
km = KMeans(n_clusters=3)
km.fit(X)
pred = km.predict(X)
/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 [8]:
sns.scatterplot(x=X[0],y=X[1], hue= pred)
Out[8]:
<Axes: xlabel='0', ylabel='1'>
In [9]:
km = KMeans(n_clusters=5)
km.fit(X)
pred = km.predict(X)
/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 [10]:
sns.scatterplot(x=X[0],y=X[1], hue= pred)
Out[10]:
<Axes: xlabel='0', ylabel='1'>
In [11]:
# 평가값: 하나의 클러스터안에 중심점으로 부터 각각의 데이터 거리를 합한 값의 평균
km.inertia_
Out[11]:
161.1727629941226
In [12]:
inertia_list = []
for i in range(2,11):
km = KMeans(n_clusters=i)
km.fit(X)
inertia_list.append(km.inertia_)
inertia_list
/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(
Out[12]:
[1578.856698952461, 222.7636237762, 187.54774629439763, 158.76742651218515, 131.59960731895123, 110.35591793460515, 90.86395201886384, 80.46931636525548, 73.72120226878646]
In [13]:
inertia_list
Out[13]:
[1578.856698952461, 222.7636237762, 187.54774629439763, 158.76742651218515, 131.59960731895123, 110.35591793460515, 90.86395201886384, 80.46931636525548, 73.72120226878646]
In [14]:
sns.lineplot(x=range(2,11),y=inertia_list) # 엘보우 메서드
Out[14]:
<Axes: >
2. marketing 데이터셋¶
In [16]:
mkt_df = pd.read_csv('/content/drive/MyDrive/KDT 시즌3/6. 머신러닝과 딥러닝/Data/marketing.csv')
In [17]:
mkt_df
Out[17]:
| ID | Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Dt_Customer | Recency | MntWines | ... | MntMeatProducts | MntFishProducts | MntSweetProducts | MntGoldProds | NumDealsPurchases | NumWebPurchases | NumCatalogPurchases | NumStorePurchases | NumWebVisitsMonth | Complain | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 5524 | 1957 | Graduation | Single | 58138.0 | 0 | 0 | 04-09-2012 | 58 | 635 | ... | 546 | 172 | 88 | 88 | 3 | 8 | 10 | 4 | 7 | 0 |
| 1 | 2174 | 1954 | Graduation | Single | 46344.0 | 1 | 1 | 08-03-2014 | 38 | 11 | ... | 6 | 2 | 1 | 6 | 2 | 1 | 1 | 2 | 5 | 0 |
| 2 | 4141 | 1965 | Graduation | Together | 71613.0 | 0 | 0 | 21-08-2013 | 26 | 426 | ... | 127 | 111 | 21 | 42 | 1 | 8 | 2 | 10 | 4 | 0 |
| 3 | 6182 | 1984 | Graduation | Together | 26646.0 | 1 | 0 | 10-02-2014 | 26 | 11 | ... | 20 | 10 | 3 | 5 | 2 | 2 | 0 | 4 | 6 | 0 |
| 4 | 5324 | 1981 | PhD | Married | 58293.0 | 1 | 0 | 19-01-2014 | 94 | 173 | ... | 118 | 46 | 27 | 15 | 5 | 5 | 3 | 6 | 5 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2235 | 10870 | 1967 | Graduation | Married | 61223.0 | 0 | 1 | 13-06-2013 | 46 | 709 | ... | 182 | 42 | 118 | 247 | 2 | 9 | 3 | 4 | 5 | 0 |
| 2236 | 4001 | 1946 | PhD | Together | 64014.0 | 2 | 1 | 10-06-2014 | 56 | 406 | ... | 30 | 0 | 0 | 8 | 7 | 8 | 2 | 5 | 7 | 0 |
| 2237 | 7270 | 1981 | Graduation | Divorced | 56981.0 | 0 | 0 | 25-01-2014 | 91 | 908 | ... | 217 | 32 | 12 | 24 | 1 | 2 | 3 | 13 | 6 | 0 |
| 2238 | 8235 | 1956 | Master | Together | 69245.0 | 0 | 1 | 24-01-2014 | 8 | 428 | ... | 214 | 80 | 30 | 61 | 2 | 6 | 5 | 10 | 3 | 0 |
| 2239 | 9405 | 1954 | PhD | Married | 52869.0 | 1 | 1 | 15-10-2012 | 40 | 84 | ... | 61 | 2 | 1 | 21 | 3 | 3 | 1 | 4 | 7 | 0 |
2240 rows × 21 columns
In [18]:
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
- 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 구매액
- MntFishProducts: 어류 구매액
- MntSweetProducts : 단맛 제품 구매액
- MntGoldProds : 골드 제품 구매액
- NumDealsPurchases: 할인 행사 구매 수
- NumWebPurchases: 웹에서 구매 수
- NumCatalogPurchases: 카탈로그에서 구매 수
- NumStorePurchases: 매장에서의 구매 수
- NumWebVisitsMonth: 월별 웹 방문 수
- Complain: 불만 여부
In [19]:
mkt_df.drop('ID',axis=1, inplace=True)
In [20]:
mkt_df.describe()
Out[20]:
| 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 [21]:
mkt_df.sort_values('Year_Birth') # 나이 오름차순
Out[21]:
| 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 [22]:
mkt_df.sort_values('Income') # 수입 오름차순
Out[22]:
| Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Dt_Customer | Recency | MntWines | MntFruits | MntMeatProducts | MntFishProducts | MntSweetProducts | MntGoldProds | NumDealsPurchases | NumWebPurchases | NumCatalogPurchases | NumStorePurchases | NumWebVisitsMonth | Complain | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1245 | 1971 | Graduation | Divorced | 1730.0 | 0 | 0 | 18-05-2014 | 65 | 1 | 1 | 3 | 1 | 1 | 1 | 15 | 0 | 0 | 0 | 20 | 0 |
| 21 | 1979 | Graduation | Married | 2447.0 | 1 | 0 | 06-01-2013 | 42 | 1 | 1 | 1725 | 1 | 1 | 1 | 15 | 0 | 28 | 0 | 1 | 0 |
| 1524 | 1973 | Graduation | Single | 3502.0 | 1 | 0 | 13-04-2013 | 56 | 2 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 14 | 0 |
| 1846 | 1963 | PhD | Married | 4023.0 | 1 | 1 | 23-06-2014 | 29 | 5 | 0 | 1 | 1 | 1 | 1 | 15 | 0 | 0 | 0 | 19 | 0 |
| 1975 | 1969 | Graduation | Married | 4428.0 | 0 | 1 | 05-10-2013 | 0 | 16 | 4 | 12 | 2 | 4 | 321 | 0 | 25 | 0 | 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 |
2240 rows × 20 columns
In [23]:
mkt_df.sort_values('Income',ascending=False) # 수입 내림차순
Out[23]:
| 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 |
2240 rows × 20 columns
In [24]:
# mkt_df = mkt_df[mkt_df['Income'] < 200000] # NaN은 저장되지 않음
mkt_df = mkt_df[mkt_df['Income'] != 666666]
In [25]:
mkt_df.sort_values('Income',ascending=False) # 수입 내림차순
Out[25]:
| 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 |
2239 rows × 20 columns
In [26]:
mkt_df.isna().mean()
Out[26]:
Year_Birth 0.000000 Education 0.000000 Marital_Status 0.000000 Income 0.010719 Kidhome 0.000000 Teenhome 0.000000 Dt_Customer 0.000000 Recency 0.000000 MntWines 0.000000 MntFruits 0.000000 MntMeatProducts 0.000000 MntFishProducts 0.000000 MntSweetProducts 0.000000 MntGoldProds 0.000000 NumDealsPurchases 0.000000 NumWebPurchases 0.000000 NumCatalogPurchases 0.000000 NumStorePurchases 0.000000 NumWebVisitsMonth 0.000000 Complain 0.000000 dtype: float64
In [27]:
mkt_df = mkt_df.dropna()
In [28]:
mkt_df.isna().mean()
Out[28]:
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 [29]:
mkt_df.info()
<class 'pandas.core.frame.DataFrame'> Index: 2215 entries, 0 to 2239 Data columns (total 20 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Year_Birth 2215 non-null int64 1 Education 2215 non-null object 2 Marital_Status 2215 non-null object 3 Income 2215 non-null float64 4 Kidhome 2215 non-null int64 5 Teenhome 2215 non-null int64 6 Dt_Customer 2215 non-null object 7 Recency 2215 non-null int64 8 MntWines 2215 non-null int64 9 MntFruits 2215 non-null int64 10 MntMeatProducts 2215 non-null int64 11 MntFishProducts 2215 non-null int64 12 MntSweetProducts 2215 non-null int64 13 MntGoldProds 2215 non-null int64 14 NumDealsPurchases 2215 non-null int64 15 NumWebPurchases 2215 non-null int64 16 NumCatalogPurchases 2215 non-null int64 17 NumStorePurchases 2215 non-null int64 18 NumWebVisitsMonth 2215 non-null int64 19 Complain 2215 non-null int64 dtypes: float64(1), int64(16), object(3) memory usage: 363.4+ KB
In [30]:
mkt_df['Dt_Customer'] = pd.to_datetime(mkt_df['Dt_Customer'], format = '%d-%m-%Y')
In [31]:
mkt_df.info()
<class 'pandas.core.frame.DataFrame'> Index: 2215 entries, 0 to 2239 Data columns (total 20 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Year_Birth 2215 non-null int64 1 Education 2215 non-null object 2 Marital_Status 2215 non-null object 3 Income 2215 non-null float64 4 Kidhome 2215 non-null int64 5 Teenhome 2215 non-null int64 6 Dt_Customer 2215 non-null datetime64[ns] 7 Recency 2215 non-null int64 8 MntWines 2215 non-null int64 9 MntFruits 2215 non-null int64 10 MntMeatProducts 2215 non-null int64 11 MntFishProducts 2215 non-null int64 12 MntSweetProducts 2215 non-null int64 13 MntGoldProds 2215 non-null int64 14 NumDealsPurchases 2215 non-null int64 15 NumWebPurchases 2215 non-null int64 16 NumCatalogPurchases 2215 non-null int64 17 NumStorePurchases 2215 non-null int64 18 NumWebVisitsMonth 2215 non-null int64 19 Complain 2215 non-null int64 dtypes: datetime64[ns](1), float64(1), int64(16), object(2) memory usage: 363.4+ KB
In [39]:
# 파생 변수 만들기
# 마지막으로 가입된 사람을 기준으로 데이터의 가입 날짜 (달)의 차를 구하기
# pass_montH
# mkt_df['pass_month'] = (mkt_df['Dt_Customer'].max()[0] - mkt_df['Dt_Customer'].max()[1])
In [52]:
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 )
mkt_df.head()
Out[52]:
| Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Dt_Customer | Recency | MntWines | MntFruits | ... | MntFishProducts | MntSweetProducts | MntGoldProds | NumDealsPurchases | NumWebPurchases | NumCatalogPurchases | NumStorePurchases | NumWebVisitsMonth | Complain | pass_month | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1957 | Graduation | Single | 58138.0 | 0 | 0 | 2012-09-04 | 58 | 635 | 88 | ... | 172 | 88 | 88 | 3 | 8 | 10 | 4 | 7 | 0 | 21 |
| 1 | 1954 | Graduation | Single | 46344.0 | 1 | 1 | 2014-03-08 | 38 | 11 | 1 | ... | 2 | 1 | 6 | 2 | 1 | 1 | 2 | 5 | 0 | 3 |
| 2 | 1965 | Graduation | Together | 71613.0 | 0 | 0 | 2013-08-21 | 26 | 426 | 49 | ... | 111 | 21 | 42 | 1 | 8 | 2 | 10 | 4 | 0 | 10 |
| 3 | 1984 | Graduation | Together | 26646.0 | 1 | 0 | 2014-02-10 | 26 | 11 | 4 | ... | 10 | 3 | 5 | 2 | 2 | 0 | 4 | 6 | 0 | 4 |
| 4 | 1981 | PhD | Married | 58293.0 | 1 | 0 | 2014-01-19 | 94 | 173 | 43 | ... | 46 | 27 | 15 | 5 | 5 | 3 | 6 | 5 | 0 | 5 |
5 rows × 21 columns
In [53]:
mkt_df.drop('Dt_Customer', axis=1, inplace=True)
In [54]:
mkt_df.head()
Out[54]:
| Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Recency | MntWines | MntFruits | MntMeatProducts | MntFishProducts | MntSweetProducts | MntGoldProds | NumDealsPurchases | NumWebPurchases | NumCatalogPurchases | NumStorePurchases | NumWebVisitsMonth | Complain | pass_month | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1957 | Graduation | Single | 58138.0 | 0 | 0 | 58 | 635 | 88 | 546 | 172 | 88 | 88 | 3 | 8 | 10 | 4 | 7 | 0 | 21 |
| 1 | 1954 | Graduation | Single | 46344.0 | 1 | 1 | 38 | 11 | 1 | 6 | 2 | 1 | 6 | 2 | 1 | 1 | 2 | 5 | 0 | 3 |
| 2 | 1965 | Graduation | Together | 71613.0 | 0 | 0 | 26 | 426 | 49 | 127 | 111 | 21 | 42 | 1 | 8 | 2 | 10 | 4 | 0 | 10 |
| 3 | 1984 | Graduation | Together | 26646.0 | 1 | 0 | 26 | 11 | 4 | 20 | 10 | 3 | 5 | 2 | 2 | 0 | 4 | 6 | 0 | 4 |
| 4 | 1981 | PhD | Married | 58293.0 | 1 | 0 | 94 | 173 | 43 | 118 | 46 | 27 | 15 | 5 | 5 | 3 | 6 | 5 | 0 | 5 |
In [55]:
# 와인 + 과일 + 육류 + 어류 + 단맛 + 골드
# Total_mnt
mkt_df['Total_mnt'] = mkt_df[['MntWines','MntFruits','MntMeatProducts','MntFishProducts','MntSweetProducts','MntGoldProds']].sum(axis=1)
mkt_df.head()
Out[55]:
| Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Recency | MntWines | MntFruits | MntMeatProducts | ... | MntSweetProducts | MntGoldProds | NumDealsPurchases | NumWebPurchases | NumCatalogPurchases | NumStorePurchases | NumWebVisitsMonth | Complain | pass_month | Total_mnt | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1957 | Graduation | Single | 58138.0 | 0 | 0 | 58 | 635 | 88 | 546 | ... | 88 | 88 | 3 | 8 | 10 | 4 | 7 | 0 | 21 | 1617 |
| 1 | 1954 | Graduation | Single | 46344.0 | 1 | 1 | 38 | 11 | 1 | 6 | ... | 1 | 6 | 2 | 1 | 1 | 2 | 5 | 0 | 3 | 27 |
| 2 | 1965 | Graduation | Together | 71613.0 | 0 | 0 | 26 | 426 | 49 | 127 | ... | 21 | 42 | 1 | 8 | 2 | 10 | 4 | 0 | 10 | 776 |
| 3 | 1984 | Graduation | Together | 26646.0 | 1 | 0 | 26 | 11 | 4 | 20 | ... | 3 | 5 | 2 | 2 | 0 | 4 | 6 | 0 | 4 | 53 |
| 4 | 1981 | PhD | Married | 58293.0 | 1 | 0 | 94 | 173 | 43 | 118 | ... | 27 | 15 | 5 | 5 | 3 | 6 | 5 | 0 | 5 | 422 |
5 rows × 21 columns
In [56]:
pd.set_option('display.max_columns',30)
In [57]:
mkt_df.head()
Out[57]:
| Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Recency | MntWines | MntFruits | MntMeatProducts | MntFishProducts | MntSweetProducts | MntGoldProds | NumDealsPurchases | NumWebPurchases | NumCatalogPurchases | NumStorePurchases | NumWebVisitsMonth | Complain | pass_month | Total_mnt | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1957 | Graduation | Single | 58138.0 | 0 | 0 | 58 | 635 | 88 | 546 | 172 | 88 | 88 | 3 | 8 | 10 | 4 | 7 | 0 | 21 | 1617 |
| 1 | 1954 | Graduation | Single | 46344.0 | 1 | 1 | 38 | 11 | 1 | 6 | 2 | 1 | 6 | 2 | 1 | 1 | 2 | 5 | 0 | 3 | 27 |
| 2 | 1965 | Graduation | Together | 71613.0 | 0 | 0 | 26 | 426 | 49 | 127 | 111 | 21 | 42 | 1 | 8 | 2 | 10 | 4 | 0 | 10 | 776 |
| 3 | 1984 | Graduation | Together | 26646.0 | 1 | 0 | 26 | 11 | 4 | 20 | 10 | 3 | 5 | 2 | 2 | 0 | 4 | 6 | 0 | 4 | 53 |
| 4 | 1981 | PhD | Married | 58293.0 | 1 | 0 | 94 | 173 | 43 | 118 | 46 | 27 | 15 | 5 | 5 | 3 | 6 | 5 | 0 | 5 | 422 |
In [58]:
mkt_df.info()
<class 'pandas.core.frame.DataFrame'> Index: 2215 entries, 0 to 2239 Data columns (total 21 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Year_Birth 2215 non-null int64 1 Education 2215 non-null object 2 Marital_Status 2215 non-null object 3 Income 2215 non-null float64 4 Kidhome 2215 non-null int64 5 Teenhome 2215 non-null int64 6 Recency 2215 non-null int64 7 MntWines 2215 non-null int64 8 MntFruits 2215 non-null int64 9 MntMeatProducts 2215 non-null int64 10 MntFishProducts 2215 non-null int64 11 MntSweetProducts 2215 non-null int64 12 MntGoldProds 2215 non-null int64 13 NumDealsPurchases 2215 non-null int64 14 NumWebPurchases 2215 non-null int64 15 NumCatalogPurchases 2215 non-null int64 16 NumStorePurchases 2215 non-null int64 17 NumWebVisitsMonth 2215 non-null int64 18 Complain 2215 non-null int64 19 pass_month 2215 non-null int32 20 Total_mnt 2215 non-null int64 dtypes: float64(1), int32(1), int64(17), object(2) memory usage: 372.1+ KB
In [59]:
mkt_df['Children'] = mkt_df[['Kidhome','Teenhome']] .sum(axis=1)
mkt_df.head()
Out[59]:
| Year_Birth | Education | Marital_Status | Income | Kidhome | Teenhome | Recency | MntWines | MntFruits | MntMeatProducts | MntFishProducts | MntSweetProducts | MntGoldProds | NumDealsPurchases | NumWebPurchases | NumCatalogPurchases | NumStorePurchases | NumWebVisitsMonth | Complain | 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 | 0 | 21 | 1617 | 0 |
| 1 | 1954 | Graduation | Single | 46344.0 | 1 | 1 | 38 | 11 | 1 | 6 | 2 | 1 | 6 | 2 | 1 | 1 | 2 | 5 | 0 | 3 | 27 | 2 |
| 2 | 1965 | Graduation | Together | 71613.0 | 0 | 0 | 26 | 426 | 49 | 127 | 111 | 21 | 42 | 1 | 8 | 2 | 10 | 4 | 0 | 10 | 776 | 0 |
| 3 | 1984 | Graduation | Together | 26646.0 | 1 | 0 | 26 | 11 | 4 | 20 | 10 | 3 | 5 | 2 | 2 | 0 | 4 | 6 | 0 | 4 | 53 | 1 |
| 4 | 1981 | PhD | Married | 58293.0 | 1 | 0 | 94 | 173 | 43 | 118 | 46 | 27 | 15 | 5 | 5 | 3 | 6 | 5 | 0 | 5 | 422 | 1 |
In [60]:
mkt_df.drop(['Kidhome','Teenhome'],axis=1,inplace=True)
In [61]:
mkt_df.head()
Out[61]:
| Year_Birth | Education | Marital_Status | Income | Recency | MntWines | MntFruits | MntMeatProducts | MntFishProducts | MntSweetProducts | MntGoldProds | NumDealsPurchases | NumWebPurchases | NumCatalogPurchases | NumStorePurchases | NumWebVisitsMonth | Complain | pass_month | Total_mnt | Children | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1957 | Graduation | Single | 58138.0 | 58 | 635 | 88 | 546 | 172 | 88 | 88 | 3 | 8 | 10 | 4 | 7 | 0 | 21 | 1617 | 0 |
| 1 | 1954 | Graduation | Single | 46344.0 | 38 | 11 | 1 | 6 | 2 | 1 | 6 | 2 | 1 | 1 | 2 | 5 | 0 | 3 | 27 | 2 |
| 2 | 1965 | Graduation | Together | 71613.0 | 26 | 426 | 49 | 127 | 111 | 21 | 42 | 1 | 8 | 2 | 10 | 4 | 0 | 10 | 776 | 0 |
| 3 | 1984 | Graduation | Together | 26646.0 | 26 | 11 | 4 | 20 | 10 | 3 | 5 | 2 | 2 | 0 | 4 | 6 | 0 | 4 | 53 | 1 |
| 4 | 1981 | PhD | Married | 58293.0 | 94 | 173 | 43 | 118 | 46 | 27 | 15 | 5 | 5 | 3 | 6 | 5 | 0 | 5 | 422 | 1 |
In [62]:
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 [63]:
mkt_df['Marital_Status'].value_counts()
Out[63]:
Marital_Status Partner 1429 Single 786 Name: count, dtype: int64
In [64]:
mkt_df.info()
<class 'pandas.core.frame.DataFrame'> Index: 2215 entries, 0 to 2239 Data columns (total 20 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Year_Birth 2215 non-null int64 1 Education 2215 non-null object 2 Marital_Status 2215 non-null object 3 Income 2215 non-null float64 4 Recency 2215 non-null int64 5 MntWines 2215 non-null int64 6 MntFruits 2215 non-null int64 7 MntMeatProducts 2215 non-null int64 8 MntFishProducts 2215 non-null int64 9 MntSweetProducts 2215 non-null int64 10 MntGoldProds 2215 non-null int64 11 NumDealsPurchases 2215 non-null int64 12 NumWebPurchases 2215 non-null int64 13 NumCatalogPurchases 2215 non-null int64 14 NumStorePurchases 2215 non-null int64 15 NumWebVisitsMonth 2215 non-null int64 16 Complain 2215 non-null int64 17 pass_month 2215 non-null int32 18 Total_mnt 2215 non-null int64 19 Children 2215 non-null int64 dtypes: float64(1), int32(1), int64(16), object(2) memory usage: 354.7+ KB
In [65]:
mkt_df = pd.get_dummies(mkt_df, columns=['Education', 'Marital_Status'])
mkt_df.head()
Out[65]:
| 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 | 1957 | 58138.0 | 58 | 635 | 88 | 546 | 172 | 88 | 88 | 3 | 8 | 10 | 4 | 7 | 0 | 21 | 1617 | 0 | False | False | True | False | False | False | True |
| 1 | 1954 | 46344.0 | 38 | 11 | 1 | 6 | 2 | 1 | 6 | 2 | 1 | 1 | 2 | 5 | 0 | 3 | 27 | 2 | False | False | True | False | False | False | True |
| 2 | 1965 | 71613.0 | 26 | 426 | 49 | 127 | 111 | 21 | 42 | 1 | 8 | 2 | 10 | 4 | 0 | 10 | 776 | 0 | False | False | True | False | False | True | False |
| 3 | 1984 | 26646.0 | 26 | 11 | 4 | 20 | 10 | 3 | 5 | 2 | 2 | 0 | 4 | 6 | 0 | 4 | 53 | 1 | False | False | True | False | False | True | False |
| 4 | 1981 | 58293.0 | 94 | 173 | 43 | 118 | 46 | 27 | 15 | 5 | 5 | 3 | 6 | 5 | 0 | 5 | 422 | 1 | False | False | False | False | True | True | False |
In [66]:
from sklearn.preprocessing import StandardScaler
In [67]:
ss = StandardScaler()
In [68]:
ss.fit_transform(mkt_df)
Out[68]:
array([[-0.98601594, 0.28660409, 0.31011292, ..., -0.52668141,
-1.34835684, 1.34835684],
[-1.23634359, -0.26140705, -0.38089872, ..., -0.52668141,
-1.34835684, 1.34835684],
[-0.31847554, 0.91272331, -0.79550571, ..., -0.52668141,
0.74164344, -0.74164344],
...,
[ 1.01660526, 0.2328438 , 1.45028214, ..., -0.52668141,
-1.34835684, 1.34835684],
[-1.06945849, 0.80269361, -1.41741619, ..., -0.52668141,
0.74164344, -0.74164344],
[-1.23634359, 0.0417787 , -0.31179756, ..., 1.89868102,
0.74164344, -0.74164344]])
In [69]:
pd.DataFrame(ss.fit_transform(mkt_df))
Out[69]:
| 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 | -0.986016 | 0.286604 | 0.310113 | 0.977779 | 1.548973 | 1.689714 | 2.453932 | 1.484340 | 0.849633 | 0.352088 | 1.428103 | 2.504074 | -0.554500 | 0.693215 | -0.097834 | 1.474609 | 1.675011 | -1.264487 | -0.315049 | -0.158077 | 0.993251 | -0.444182 | -0.526681 | -1.348357 | 1.348357 |
| 1 | -1.236344 | -0.261407 | -0.380899 | -0.872375 | -0.637338 | -0.718196 | -0.651178 | -0.634081 | -0.733043 | -0.167829 | -1.125846 | -0.571253 | -1.169839 | -0.131420 | -0.097834 | -1.231986 | -0.962727 | 1.405522 | -0.315049 | -0.158077 | 0.993251 | -0.444182 | -0.526681 | -1.348357 | 1.348357 |
| 2 | -0.318476 | 0.912723 | -0.795506 | 0.358096 | 0.568903 | -0.178646 | 1.339745 | -0.147087 | -0.038210 | -0.687745 | 1.428103 | -0.229550 | 1.291518 | -0.543738 | -0.097834 | -0.179421 | 0.279830 | -1.264487 | -0.315049 | -0.158077 | 0.993251 | -0.444182 | -0.526681 | 0.741643 | -0.741643 |
| 3 | 1.266933 | -1.176680 | -0.795506 | -0.872375 | -0.561948 | -0.655768 | -0.505056 | -0.585381 | -0.752344 | -0.167829 | -0.760996 | -0.912956 | -0.554500 | 0.280897 | -0.097834 | -1.081620 | -0.919594 | 0.070517 | -0.315049 | -0.158077 | 0.993251 | -0.444182 | -0.526681 | 0.741643 | -0.741643 |
| 4 | 1.016605 | 0.293806 | 1.553934 | -0.392047 | 0.418123 | -0.218777 | 0.152497 | -0.000989 | -0.559335 | 1.391921 | 0.333553 | 0.112153 | 0.060839 | -0.131420 | -0.097834 | -0.931253 | -0.307440 | 0.070517 | -0.315049 | -0.158077 | -1.006795 | -0.444182 | 1.898681 | 0.741643 | -0.741643 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2210 | -0.151590 | 0.429949 | -0.104494 | 1.197188 | 0.418123 | 0.066605 | 0.079436 | 2.214830 | 3.918482 | -0.167829 | 1.792953 | 0.112153 | -0.554500 | -0.131420 | -0.097834 | 0.121311 | 1.217140 | 0.070517 | -0.315049 | -0.158077 | 0.993251 | -0.444182 | -0.526681 | 0.741643 | -0.741643 |
| 2211 | -1.903884 | 0.559634 | 0.241012 | 0.298796 | -0.662468 | -0.611177 | -0.687709 | -0.658430 | -0.694442 | 2.431754 | 1.428103 | -0.229550 | -0.246830 | 0.693215 | -0.097834 | -1.683085 | -0.270943 | 2.740526 | -0.315049 | -0.158077 | -1.006795 | -0.444182 | 1.898681 | 0.741643 | -0.741643 |
| 2212 | 1.016605 | 0.232844 | 1.450282 | 1.787221 | 0.543773 | 0.222673 | -0.103218 | -0.366234 | -0.385627 | -0.687745 | -0.760996 | 0.112153 | 2.214527 | 0.280897 | -0.097834 | -0.931253 | 1.051244 | -1.264487 | -0.315049 | -0.158077 | 0.993251 | -0.444182 | -0.526681 | -1.348357 | 1.348357 |
| 2213 | -1.069458 | 0.802694 | -1.417416 | 0.364026 | 0.091432 | 0.209295 | 0.773519 | 0.072060 | 0.328508 | -0.167829 | 0.698403 | 0.795559 | 1.291518 | -0.956055 | -0.097834 | -0.931253 | 0.390980 | 0.070517 | -0.315049 | -0.158077 | -1.006795 | 2.251331 | -0.526681 | 0.741643 | -0.741643 |
| 2214 | -1.236344 | 0.041779 | -0.311798 | -0.655931 | -0.587078 | -0.472946 | -0.651178 | -0.634081 | -0.443529 | 0.352088 | -0.396146 | -0.571253 | -0.554500 | 0.693215 | -0.097834 | 1.324242 | -0.722179 | 1.405522 | -0.315049 | -0.158077 | -1.006795 | -0.444182 | 1.898681 | 0.741643 | -0.741643 |
2215 rows × 25 columns
In [71]:
ss_df = pd.DataFrame(ss.fit_transform(mkt_df),columns=mkt_df.columns)
ss_df
Out[71]:
| 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 | -0.986016 | 0.286604 | 0.310113 | 0.977779 | 1.548973 | 1.689714 | 2.453932 | 1.484340 | 0.849633 | 0.352088 | 1.428103 | 2.504074 | -0.554500 | 0.693215 | -0.097834 | 1.474609 | 1.675011 | -1.264487 | -0.315049 | -0.158077 | 0.993251 | -0.444182 | -0.526681 | -1.348357 | 1.348357 |
| 1 | -1.236344 | -0.261407 | -0.380899 | -0.872375 | -0.637338 | -0.718196 | -0.651178 | -0.634081 | -0.733043 | -0.167829 | -1.125846 | -0.571253 | -1.169839 | -0.131420 | -0.097834 | -1.231986 | -0.962727 | 1.405522 | -0.315049 | -0.158077 | 0.993251 | -0.444182 | -0.526681 | -1.348357 | 1.348357 |
| 2 | -0.318476 | 0.912723 | -0.795506 | 0.358096 | 0.568903 | -0.178646 | 1.339745 | -0.147087 | -0.038210 | -0.687745 | 1.428103 | -0.229550 | 1.291518 | -0.543738 | -0.097834 | -0.179421 | 0.279830 | -1.264487 | -0.315049 | -0.158077 | 0.993251 | -0.444182 | -0.526681 | 0.741643 | -0.741643 |
| 3 | 1.266933 | -1.176680 | -0.795506 | -0.872375 | -0.561948 | -0.655768 | -0.505056 | -0.585381 | -0.752344 | -0.167829 | -0.760996 | -0.912956 | -0.554500 | 0.280897 | -0.097834 | -1.081620 | -0.919594 | 0.070517 | -0.315049 | -0.158077 | 0.993251 | -0.444182 | -0.526681 | 0.741643 | -0.741643 |
| 4 | 1.016605 | 0.293806 | 1.553934 | -0.392047 | 0.418123 | -0.218777 | 0.152497 | -0.000989 | -0.559335 | 1.391921 | 0.333553 | 0.112153 | 0.060839 | -0.131420 | -0.097834 | -0.931253 | -0.307440 | 0.070517 | -0.315049 | -0.158077 | -1.006795 | -0.444182 | 1.898681 | 0.741643 | -0.741643 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2210 | -0.151590 | 0.429949 | -0.104494 | 1.197188 | 0.418123 | 0.066605 | 0.079436 | 2.214830 | 3.918482 | -0.167829 | 1.792953 | 0.112153 | -0.554500 | -0.131420 | -0.097834 | 0.121311 | 1.217140 | 0.070517 | -0.315049 | -0.158077 | 0.993251 | -0.444182 | -0.526681 | 0.741643 | -0.741643 |
| 2211 | -1.903884 | 0.559634 | 0.241012 | 0.298796 | -0.662468 | -0.611177 | -0.687709 | -0.658430 | -0.694442 | 2.431754 | 1.428103 | -0.229550 | -0.246830 | 0.693215 | -0.097834 | -1.683085 | -0.270943 | 2.740526 | -0.315049 | -0.158077 | -1.006795 | -0.444182 | 1.898681 | 0.741643 | -0.741643 |
| 2212 | 1.016605 | 0.232844 | 1.450282 | 1.787221 | 0.543773 | 0.222673 | -0.103218 | -0.366234 | -0.385627 | -0.687745 | -0.760996 | 0.112153 | 2.214527 | 0.280897 | -0.097834 | -0.931253 | 1.051244 | -1.264487 | -0.315049 | -0.158077 | 0.993251 | -0.444182 | -0.526681 | -1.348357 | 1.348357 |
| 2213 | -1.069458 | 0.802694 | -1.417416 | 0.364026 | 0.091432 | 0.209295 | 0.773519 | 0.072060 | 0.328508 | -0.167829 | 0.698403 | 0.795559 | 1.291518 | -0.956055 | -0.097834 | -0.931253 | 0.390980 | 0.070517 | -0.315049 | -0.158077 | -1.006795 | 2.251331 | -0.526681 | 0.741643 | -0.741643 |
| 2214 | -1.236344 | 0.041779 | -0.311798 | -0.655931 | -0.587078 | -0.472946 | -0.651178 | -0.634081 | -0.443529 | 0.352088 | -0.396146 | -0.571253 | -0.554500 | 0.693215 | -0.097834 | 1.324242 | -0.722179 | 1.405522 | -0.315049 | -0.158077 | -1.006795 | -0.444182 | 1.898681 | 0.741643 | -0.741643 |
2215 rows × 25 columns
3. KMeans¶
- k 개의 중심점을 찍은 후에 이 중심점에서 각 점간의 거리의 합이 가장 최소가 되는 중심점 k 의 위치를 찾고, 이 중심점에서 가까운 점들을 중심점을 기준으로 묶는 알고리즘
- k 개의 클러스터의 수는 정해줘야함
In [72]:
inertia_list = []
for i in range(2,11):
km = KMeans(n_clusters=i, random_state=2024)
km.fit(ss_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 [73]:
inertia_list
Out[73]:
[42895.856234544146, 39808.76012368429, 37490.52243646169, 36318.22350604256, 34721.81554520999, 32811.087347960834, 30932.843435698545, 29861.17624413476, 28567.33866727262]
In [74]:
sns.lineplot(x=range(2,11), y=inertia_list)
Out[74]:
<Axes: >
4. 실루엣 스코어(Silhouette Score)¶
- 군집화의 품질을 평가하는 지표로 , 각 데이터 포인트가 자신이 속한 군집과 얼마나 잘 맞는지, 그리고 다른 군집과 얼마나 잘 구분되는지를 측정
- -1 에서 1 사이의 값을 가지며 , 값이 클 수록 군집화의 품질이 높음을 나타냄
In [75]:
from sklearn.metrics import silhouette_score
In [76]:
score = []
for i in range(2,11):
km = KMeans(n_clusters=i, random_state=2024)
km.fit(ss_df)
pred = km.predict(ss_df)
score.append(silhouette_score(ss_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 [77]:
score
Out[77]:
[0.23021433912466954, 0.14017899641618797, 0.1573165286310758, 0.12325837868572198, 0.11637407813605051, 0.15042645154594841, 0.1369180265149236, 0.13876322067813077, 0.14964165131428742]
In [78]:
sns.lineplot(x=range(2,11), y=score)
Out[78]:
<Axes: >
In [79]:
km = KMeans(n_clusters=4, random_state=2024)
km.fit(ss_df)
/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(
Out[79]:
KMeans(n_clusters=4, random_state=2024)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
KMeans(n_clusters=4, random_state=2024)
In [80]:
pred = km.predict(ss_df)
pred
Out[80]:
array([2, 1, 2, ..., 2, 3, 1], dtype=int32)
In [82]:
mkt_df['label'] = pred
mkt_df
Out[82]:
| 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 | 21 | 1617 | 0 | False | False | True | False | False | False | True | 2 |
| 1 | 1954 | 46344.0 | 38 | 11 | 1 | 6 | 2 | 1 | 6 | 2 | 1 | 1 | 2 | 5 | 0 | 3 | 27 | 2 | False | False | True | False | False | False | True | 1 |
| 2 | 1965 | 71613.0 | 26 | 426 | 49 | 127 | 111 | 21 | 42 | 1 | 8 | 2 | 10 | 4 | 0 | 10 | 776 | 0 | False | False | True | False | False | True | False | 2 |
| 3 | 1984 | 26646.0 | 26 | 11 | 4 | 20 | 10 | 3 | 5 | 2 | 2 | 0 | 4 | 6 | 0 | 4 | 53 | 1 | False | False | True | False | False | True | False | 1 |
| 4 | 1981 | 58293.0 | 94 | 173 | 43 | 118 | 46 | 27 | 15 | 5 | 5 | 3 | 6 | 5 | 0 | 5 | 422 | 1 | False | False | False | False | True | True | False | 3 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2235 | 1967 | 61223.0 | 46 | 709 | 43 | 182 | 42 | 118 | 247 | 2 | 9 | 3 | 4 | 5 | 0 | 12 | 1341 | 1 | False | False | True | False | False | True | False | 2 |
| 2236 | 1946 | 64014.0 | 56 | 406 | 0 | 30 | 0 | 0 | 8 | 7 | 8 | 2 | 5 | 7 | 0 | 0 | 444 | 3 | False | False | False | False | True | True | False | 3 |
| 2237 | 1981 | 56981.0 | 91 | 908 | 48 | 217 | 32 | 12 | 24 | 1 | 2 | 3 | 13 | 6 | 0 | 5 | 1241 | 0 | False | False | True | False | False | False | True | 2 |
| 2238 | 1956 | 69245.0 | 8 | 428 | 30 | 214 | 80 | 30 | 61 | 2 | 6 | 5 | 10 | 3 | 0 | 5 | 843 | 1 | False | False | False | True | False | True | False | 3 |
| 2239 | 1954 | 52869.0 | 40 | 84 | 3 | 61 | 2 | 1 | 21 | 3 | 3 | 1 | 4 | 7 | 0 | 20 | 172 | 2 | False | False | False | False | True | True | False | 1 |
2215 rows × 26 columns
In [83]:
mkt_df['label'].value_counts()
Out[83]:
label 1 975 2 598 3 588 0 54 Name: count, dtype: int64
In [ ]: