해당 자료는 전북대학교 최규빈 교수님 2023학년도 2학기 빅데이터분석특강 자료임

03wk-011: Medical Cost, 회귀분석

최규빈
2023-09-21

1. 강의영상

https://youtu.be/playlist?list=PLQqh36zP38-zwUq3ZIN2SNas0l8htamMO&si=dH2sszdMPMFTGeEV

2. Import

import numpy as np
import pandas as pd 
import sklearn.linear_model

3. Data 불러오기

- 캐글에서 Medical Cost Personal Datasets download

https://www.kaggle.com/datasets/mirichoi0218/insurance

- Data Load

df = pd.read_csv('insurance.csv')
df

	age	sex	bmi	children	smoker	region	charges
0	19	female	27.900	0	yes	southwest	16884.92400
1	18	male	33.770	1	no	southeast	1725.55230
2	28	male	33.000	3	no	southeast	4449.46200
3	33	male	22.705	0	no	northwest	21984.47061
4	32	male	28.880	0	no	northwest	3866.85520
...	...	...	...	...	...	...	...
1333	50	male	30.970	3	no	northwest	10600.54830
1334	18	female	31.920	0	no	northeast	2205.98080
1335	18	female	36.850	0	no	southeast	1629.83350
1336	21	female	25.800	0	no	southwest	2007.94500
1337	61	female	29.070	0	yes	northwest	29141.36030

1338 rows × 7 columns

4. 분석

A. Data 정리

df.columns

Index(['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'charges'], dtype='object')

X = pd.get_dummies(df.drop(['charges'],axis=1))
y = df[['charges']]

	age	bmi	children	sex_female	sex_male	smoker_no	smoker_yes	region_northeast	region_northwest	region_southeast	region_southwest
0	19	27.900	0	1	0	0	1	0	0	0	1
1	18	33.770	1	0	1	1	0	0	0	1	0
2	28	33.000	3	0	1	1	0	0	0	1	0
3	33	22.705	0	0	1	1	0	0	1	0	0
4	32	28.880	0	0	1	1	0	0	1	0	0
...	...	...	...	...	...	...	...	...	...	...	...
1333	50	30.970	3	0	1	1	0	0	1	0	0
1334	18	31.920	0	1	0	1	0	1	0	0	0
1335	18	36.850	0	1	0	1	0	0	0	1	0
1336	21	25.800	0	1	0	1	0	0	0	0	1
1337	61	29.070	0	1	0	0	1	0	1	0	0

1338 rows × 11 columns

	charges
0	16884.92400
1	1725.55230
2	4449.46200
3	21984.47061
4	3866.85520
...	...
1333	10600.54830
1334	2205.98080
1335	1629.83350
1336	2007.94500
1337	29141.36030

1338 rows × 1 columns

B. Predictor 생성

predictr = sklearn.linear_model.LinearRegression()

C. 학습

predictr.fit(X,y)

LinearRegression()

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D. 예측

df.assign(yhat = predictr.predict(X))

	age	sex	bmi	children	smoker	region	charges	yhat
0	19	female	27.900	0	yes	southwest	16884.92400	25293.713028
1	18	male	33.770	1	no	southeast	1725.55230	3448.602834
2	28	male	33.000	3	no	southeast	4449.46200	6706.988491
3	33	male	22.705	0	no	northwest	21984.47061	3754.830163
4	32	male	28.880	0	no	northwest	3866.85520	5592.493386
...	...	...	...	...	...	...	...	...
1333	50	male	30.970	3	no	northwest	10600.54830	12351.323686
1334	18	female	31.920	0	no	northeast	2205.98080	3511.930809
1335	18	female	36.850	0	no	southeast	1629.83350	4149.132486
1336	21	female	25.800	0	no	southwest	2007.94500	1246.584939
1337	61	female	29.070	0	yes	northwest	29141.36030	37085.623268

1338 rows × 8 columns

E. 평가

predictr.score(X,y) # R^2

0.7509130345985207

0.7 이상이면 망한모형까지는 아님 (대회용으로는 부적절할 수 있으나 대충 쓸 수는 있는 정도)

5. 계수해석

- 상수항 해석

predictr.intercept_

array([-666.93771994])

기본적인 보험료는 -666이라는 의미

- 계수해석

pd.DataFrame({'name':list(X.columns), 'coef':predictr.coef_.reshape(-1)})

	name	coef
0	age	256.856353
1	bmi	339.193454
2	children	475.500545
3	sex_female	65.657180
4	sex_male	-65.657180
5	smoker_no	-11924.267271
6	smoker_yes	11924.267271
7	region_northeast	587.009235
8	region_northwest	234.045336
9	region_southeast	-448.012814
10	region_southwest	-373.041756

지역은 잘 모르겠으나 나머지는 꽤 그럴듯해 보임
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