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Datasets:
LevyJonas
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health_care_survay

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ADM_RNO1
int64
21
1,000k
Province
int64
10
60
Health_region_grouped
int64
10.9k
60.9k
Gender
float64
1
2
Marital_status
float64
1
6
⌀
Household
float64
1
2
⌀
Age
int64
1
5
Worked_job_business
float64
1
2
⌀
Edu_level
float64
1
3
⌀
Gen_health_state
float64
1
5
⌀
Life_satisfaction
float64
3
10
⌀
Mental_health_state
float64
1
5
⌀
Stress_level
float64
1
5
⌀
Work_stress
float64
1
5
⌀
Sense_belonging
float64
1
4
⌀
Weight_state
float64
1
3
⌀
BMI_12_17
float64
1
2
⌀
BMI_18_above
float64
1
2
⌀
Sleep_apnea
float64
1
2
⌀
High_BP
float64
1
2
⌀
High_cholestrol
float64
1
2
⌀
Diabetic
float64
1
9
⌀
Fatigue_syndrome
float64
1
2
⌀
Mood_disorder
float64
1
2
⌀
Anxiety_disorder
float64
1
2
⌀
Respiratory_chronic_con
float64
1
2
⌀
Musculoskeletal_con
float64
1
2
⌀
Cardiovascular_con
float64
1
2
⌀
Health_utility_indx
float64
1
2
⌀
Pain_status
float64
1
2
⌀
Act_improve_health
float64
1
9
⌀
Fruit_veg_con
float64
0
42.6
⌀
Smoked
float64
1
40
⌀
Tobaco_use
float64
1
2
⌀
weekly_alcohol
float64
0
3
⌀
Cannabies_use
float64
1
2
⌀
Drug_use
float64
1
2
⌀
Total_active_time
float64
0
2.75k
⌀
Total_physical_act_time
float64
0
2.1k
⌀
Other_physical_act_time
float64
0
930
⌀
Physical_vigorous_act_time
float64
0
400
⌀
Work_hours
float64
8
50
⌀
working_status
float64
1
2
⌀
Aboriginal_identity
float64
1
2
⌀
Birth_country
float64
1
2
⌀
Immigrant
float64
1
2
⌀
Insurance_cover
float64
1
8
⌀
Food_security
float64
0
3
⌀
Income_source
float64
1
2
⌀
Total_income
float64
1
5
⌀
1,000
47
47,906
2
1
2
3
1
3
3
9
3
2
2
2
3
null
2
2
2
2
2
2
2
2
2
2
2
1
2
null
1.3
null
null
null
2
2
0
0
60
10
38
1
2
1
2
1
0
1
5
100,005
47
47,906
1
1
2
5
null
2
3
4
3
3
null
3
1
null
2
1
1
2
2
2
1
2
2
2
2
null
1
null
1
null
null
null
2
2
0
0
0
0
null
null
2
1
2
1
0
2
4
100,012
59
59,914
2
2
1
5
null
1
2
7
3
3
null
2
1
null
2
2
1
2
1
2
2
2
1
1
2
1
1
null
null
null
2
null
2
2
null
null
null
null
null
null
2
1
2
6
null
2
2
100,015
13
13,904
1
2
1
5
null
1
3
8
3
3
null
2
1
null
2
2
1
2
2
2
2
2
2
1
2
2
1
null
null
null
null
null
2
null
null
null
null
null
null
null
2
1
2
6
0
2
3
100,018
46
46,903
1
2
1
4
2
3
5
null
5
4
null
3
3
null
2
2
1
2
9
2
2
2
null
1
null
1
2
null
null
null
null
null
2
2
null
null
null
null
null
null
2
1
2
2
0
2
1
100,030
35
35,934
2
1
2
5
null
3
3
7
2
4
null
2
1
null
2
2
2
2
2
1
2
2
2
1
1
2
2
null
null
null
null
2
2
null
null
null
null
null
null
null
2
1
2
6
0
2
5
10,005
35
35,933
1
1
2
5
null
2
2
8
3
2
null
3
1
null
2
2
1
1
2
2
2
2
2
1
1
2
1
null
null
null
null
2
2
null
null
null
null
null
null
null
2
1
2
6
0
2
2
100,051
12
12,902
1
2
1
5
null
2
2
10
1
1
null
1
1
null
2
2
2
1
1
2
2
2
2
2
2
2
1
null
7.3
null
null
null
2
null
null
null
null
null
null
null
2
1
2
1
0
2
3
100,078
24
24,909
2
1
2
5
null
1
3
8
2
1
null
1
1
null
2
2
1
1
1
2
2
2
1
1
2
1
2
2
null
null
2
null
2
null
null
null
null
null
null
null
2
1
2
6
0
2
2
100,080
12
12,903
1
1
2
5
null
3
2
10
1
1
null
1
3
null
1
2
1
1
2
2
2
2
2
2
2
2
1
null
4.4
null
null
null
2
null
null
null
null
null
null
null
2
1
2
1
0
2
5
100,086
35
35,939
2
1
2
2
1
3
2
10
2
3
2
1
3
null
1
2
2
2
2
2
2
2
2
2
2
1
2
null
null
null
null
null
2
null
null
null
null
null
50
1
2
1
2
6
0
1
3
10,009
59
59,923
1
2
1
5
null
2
3
6
3
1
null
2
1
null
2
2
2
2
1
2
2
2
2
2
2
2
1
null
null
null
2
null
2
2
null
null
null
null
null
null
2
1
2
6
null
1
4
100,093
24
24,906
2
2
2
4
1
3
2
9
2
2
2
2
1
null
2
2
1
2
2
2
2
2
2
1
2
2
1
2
null
null
2
3
2
null
null
null
null
null
35
1
2
1
2
6
0
null
2
100,104
10
10,911
1
1
2
5
null
3
1
10
1
1
null
1
3
null
1
2
2
2
2
2
2
2
2
2
2
2
1
null
null
null
null
null
2
null
null
null
null
null
null
null
2
1
2
6
0
1
5
100,105
24
24,913
2
2
2
5
null
2
3
6
3
1
null
1
3
null
1
2
2
2
2
2
2
2
2
1
2
null
1
2
null
null
2
null
2
null
null
null
null
null
null
null
2
1
2
6
0
1
5
100,129
35
35,955
2
2
1
5
null
2
3
8
2
1
null
2
3
null
2
2
1
2
2
2
2
2
2
1
2
1
2
null
null
null
null
0
2
null
null
null
null
null
null
null
2
1
2
6
0
2
3
100,143
35
35,946
2
2
1
5
null
3
1
10
2
1
null
1
1
null
2
2
1
2
2
2
2
2
2
1
2
1
1
null
null
null
null
0
2
null
null
null
null
null
null
null
2
1
2
6
0
2
2
100,147
24
24,908
1
6
2
1
null
3
3
6
1
3
null
2
3
1
null
2
2
null
2
2
2
2
2
null
2
2
1
1
null
null
2
0
1
null
null
null
null
null
null
null
2
1
2
6
0
1
3
100,148
35
35,957
1
1
2
2
2
3
2
7
4
3
null
3
3
null
2
2
2
2
2
2
2
2
2
2
2
2
2
null
null
null
null
0
1
null
null
null
null
null
null
null
2
1
2
6
0
1
3
100,154
48
48,931
1
1
2
5
null
3
4
8
3
2
null
3
1
null
2
2
2
2
2
2
2
2
2
2
2
1
1
null
2.8
null
null
null
2
2
0
20
30
10
null
null
2
1
2
6
0
1
5
100,164
48
48,933
1
1
2
4
1
3
3
5
2
4
4
2
1
null
2
2
2
2
2
2
2
2
2
1
2
null
2
null
0
null
null
null
2
2
0
0
0
0
50
1
2
1
2
6
0
1
5
100,176
59
59,932
2
2
1
2
1
3
3
7
3
3
4
2
3
null
1
2
2
2
2
2
2
2
2
2
2
2
1
null
null
null
2
null
2
2
null
null
null
null
45
1
2
1
2
6
null
1
5
10,018
13
13,902
2
1
2
5
null
3
2
8
2
2
null
2
3
null
1
2
1
2
2
1
2
2
2
null
2
null
2
null
null
null
null
null
2
null
null
null
null
null
null
null
null
1
2
6
2
null
null
100,193
47
47,904
2
1
2
5
null
3
2
8
2
3
null
1
3
null
1
2
2
1
2
2
2
2
2
1
1
null
1
null
6.4
null
null
null
2
2
0
0
30
0
null
null
2
2
1
1
0
2
4
100,196
46
46,903
2
2
2
2
1
3
2
10
1
2
2
3
3
null
2
2
2
2
2
2
2
1
2
2
2
2
1
null
null
null
null
null
1
2
null
null
null
null
40
1
2
1
2
1
0
1
5
1,002
24
24,913
1
2
1
4
1
3
3
5
3
4
4
4
1
null
2
2
2
2
2
2
2
2
null
1
2
1
1
2
null
35
2
0
1
null
null
null
null
null
39
1
2
1
2
6
2
1
4
100,205
59
59,932
1
2
1
4
2
2
2
10
2
2
null
2
3
null
2
2
2
2
2
2
2
2
2
2
2
2
1
null
null
null
2
null
1
2
null
null
null
null
null
null
2
2
1
6
null
null
2
100,215
24
24,901
1
1
2
2
1
3
3
8
1
2
2
2
1
null
2
2
2
2
2
2
2
2
2
2
2
2
1
1
null
null
2
1
2
null
null
null
null
null
40
1
2
2
1
6
0
1
5
100,218
11
11,900
1
1
2
5
null
3
3
7
3
3
null
1
3
null
1
2
2
2
2
2
1
2
2
1
2
2
1
1
3
null
null
null
2
null
0
0
0
0
null
null
2
1
2
6
0
1
5
100,232
35
35,995
2
2
1
2
1
3
1
8
4
3
3
3
null
null
null
2
2
2
2
2
2
2
2
2
2
2
1
null
null
null
null
null
2
null
null
null
null
null
null
null
2
2
1
6
2
2
2
100,240
48
48,934
1
1
2
3
1
3
2
9
1
2
3
3
3
null
1
2
2
2
2
2
2
2
2
2
2
2
1
null
2.7
null
null
null
2
2
0
90
60
0
35
1
2
2
1
6
0
1
5
100,245
35
35,966
1
2
2
2
1
3
1
9
1
3
null
2
3
null
1
2
2
2
2
2
2
2
2
2
2
1
1
null
null
null
null
1
2
null
null
null
null
null
null
null
2
1
2
6
0
1
5
100,249
24
24,916
2
2
1
5
null
2
3
5
4
1
null
2
1
null
null
2
1
1
2
2
2
2
2
2
2
1
1
2
null
null
2
1
2
null
null
null
null
null
null
null
2
1
2
6
0
2
1
100,250
48
48,932
1
1
2
4
1
3
4
10
2
2
2
1
3
null
2
2
2
2
2
2
2
2
2
2
2
2
1
null
3.1
7
null
null
2
2
245
0
0
0
40
1
2
2
1
6
null
1
2
100,256
35
35,953
1
1
2
5
null
null
4
5
1
1
null
3
1
null
2
2
1
1
1
2
2
2
2
2
1
1
2
null
null
null
null
null
2
null
null
null
null
null
null
null
2
2
1
6
3
null
5
100,281
13
13,901
2
1
2
3
1
3
2
8
3
3
3
3
3
null
1
2
2
2
2
2
2
2
2
2
2
2
1
null
null
null
null
null
2
null
null
null
null
null
38
1
2
1
2
6
0
null
5
100,291
24
24,912
1
1
2
5
null
3
1
10
1
2
null
1
3
null
2
2
2
2
2
2
2
2
2
1
2
2
1
2
null
null
2
null
2
null
null
null
null
null
null
null
2
1
2
6
0
2
4
100,302
48
48,932
1
1
2
2
1
3
2
7
1
3
3
2
3
null
2
2
2
2
2
2
2
2
2
2
2
1
2
null
8
10
null
null
1
1
0
120
null
120
50
1
2
1
2
6
0
1
5
100,308
35
35,960
1
1
2
5
null
3
2
null
null
4
null
null
null
null
null
2
1
1
2
2
2
2
1
1
2
1
1
null
null
null
null
null
null
null
null
null
null
null
null
null
2
1
2
6
0
2
5
100,310
47
47,901
1
1
2
5
null
3
2
9
2
3
null
2
3
null
2
2
2
1
2
2
2
2
2
1
2
2
1
null
1.9
null
null
null
2
2
120
180
20
0
null
null
2
1
2
2
0
2
5
100,311
24
24,901
2
2
1
5
null
3
3
7
3
3
null
2
1
null
2
1
2
2
2
2
2
2
2
2
2
2
1
1
null
null
2
0
2
null
null
null
null
null
null
null
2
1
2
6
0
2
3
100,340
35
35,939
2
1
null
4
1
3
2
8
2
3
1
1
1
null
2
1
1
1
2
2
2
2
null
2
2
2
1
null
null
null
null
0
2
null
null
null
null
null
25
2
2
1
2
6
0
null
4
100,355
24
24,903
1
1
2
5
null
3
4
null
null
2
null
null
null
null
null
2
1
2
2
2
2
2
2
2
1
1
2
9
null
null
2
null
null
null
null
null
null
null
null
null
2
1
2
6
0
2
3
100,356
35
35,962
1
2
2
2
1
3
1
9
1
3
3
4
3
null
1
2
2
2
2
2
2
2
2
2
2
2
1
null
null
null
null
null
2
null
null
null
null
null
45
1
2
1
2
6
0
1
5
100,363
48
48,934
2
2
1
5
null
3
3
7
4
3
null
2
3
null
1
1
2
2
2
1
2
2
2
1
2
1
1
null
8.9
null
null
null
2
2
60
0
50
30
null
null
2
2
1
6
0
2
4
100,392
24
24,901
1
1
2
4
1
2
3
8
1
3
3
2
1
null
2
2
2
1
1
2
2
2
2
2
null
2
1
2
null
null
2
2
2
null
null
null
null
null
40
1
2
1
2
6
0
1
5
100,402
10
10,912
1
1
2
5
null
2
5
10
1
1
null
1
1
null
2
2
1
2
1
2
2
2
null
2
1
1
2
null
null
15
null
null
null
null
null
null
null
null
null
null
2
1
2
6
0
2
2
100,411
24
24,913
2
2
1
4
1
3
1
9
2
3
1
2
3
null
1
2
2
2
2
2
2
2
2
2
2
2
1
1
null
null
2
null
null
null
null
null
null
null
39
1
2
1
2
6
0
1
5
100,424
46
46,901
2
1
2
5
null
3
3
7
2
3
null
2
1
null
2
2
1
1
2
2
2
2
2
2
2
2
1
null
null
null
null
null
2
2
null
null
null
null
null
null
2
1
2
1
0
2
2
100,440
35
35,939
2
2
1
5
null
3
1
10
1
2
null
1
3
null
2
2
2
2
2
2
2
2
2
1
2
2
1
null
null
null
null
1
2
null
null
null
null
null
null
null
2
1
2
6
0
2
3
100,447
24
24,906
1
2
1
3
1
3
1
9
1
4
5
1
3
null
2
2
2
2
2
2
2
2
2
2
2
2
1
2
null
null
2
null
1
null
null
null
null
null
45
1
2
1
2
6
0
1
5
100,451
10
10,912
2
1
2
4
2
2
4
6
4
4
null
3
1
null
2
2
1
2
9
2
1
1
2
2
2
1
1
null
null
null
null
null
2
null
null
null
null
null
null
null
2
1
2
6
0
1
5
100,494
12
12,901
2
2
1
5
null
1
5
10
3
2
null
2
1
null
2
2
1
2
2
2
2
2
2
1
2
2
2
null
2.9
null
null
null
2
null
null
null
null
null
null
null
2
1
2
1
0
2
2
100,495
59
59,931
1
2
1
2
1
3
2
8
1
4
4
2
3
null
1
2
2
2
2
2
2
2
2
2
2
2
1
null
null
null
2
null
2
2
null
null
null
null
null
null
2
2
1
6
null
1
3
100,513
59
59,942
2
1
2
2
1
3
1
9
2
2
3
1
3
null
1
2
2
2
2
2
2
2
1
2
2
2
1
null
null
null
2
null
1
2
null
null
null
null
40
1
2
1
2
6
null
1
5
100,517
24
24,915
2
1
2
5
null
1
2
9
2
2
null
2
3
null
2
2
2
2
2
2
2
2
2
1
2
2
1
2
null
null
2
2
2
null
null
null
null
null
null
null
2
1
2
6
0
1
5
100,529
59
59,932
1
1
2
5
null
3
1
8
2
3
null
3
1
null
2
1
2
2
2
2
2
2
2
1
2
1
1
null
null
null
2
null
2
2
null
null
null
null
null
null
2
1
2
6
null
1
5
100,538
10
10,912
2
1
2
4
1
2
2
8
2
3
1
1
3
null
2
2
2
1
2
2
2
2
2
1
2
2
1
null
null
null
null
null
null
null
null
null
null
null
40
1
2
1
2
6
0
1
4
100,549
13
13,902
1
2
1
2
1
3
4
10
4
3
3
2
1
null
2
2
1
2
2
2
2
1
2
2
2
null
1
null
null
null
null
null
2
null
null
null
null
null
40
1
2
1
2
6
2
1
2
100,572
60
60,901
1
1
2
2
1
3
3
7
3
4
4
2
3
null
1
2
2
2
2
2
2
2
2
null
2
2
1
null
null
null
null
null
1
null
null
null
null
null
null
null
1
1
2
1
0
1
5
100,575
48
48,935
2
6
2
1
null
3
1
10
1
2
null
1
3
null
null
2
2
null
2
2
2
2
2
null
2
2
1
null
2.4
null
null
null
2
2
null
null
null
null
null
null
2
1
2
6
0
1
5
100,578
46
46,903
1
6
2
1
null
null
3
8
3
3
null
2
1
2
null
2
2
null
2
2
2
2
2
null
2
1
2
null
null
null
null
null
null
2
null
null
null
null
null
null
null
1
2
2
0
1
4
100,590
35
35,937
1
2
1
5
null
1
1
8
1
3
null
2
1
null
2
2
2
2
2
2
2
2
2
2
2
2
1
null
null
null
null
null
2
null
null
null
null
null
null
null
2
1
2
6
0
2
2
100,592
24
24,913
2
1
2
3
1
3
1
10
1
3
3
3
1
null
2
2
2
2
2
2
2
2
2
2
2
2
1
2
null
null
2
null
2
null
null
null
null
null
null
null
2
2
1
6
0
null
5
100,599
46
46,902
1
2
2
5
null
2
4
8
3
2
null
4
3
null
1
2
1
1
2
2
2
2
2
2
2
1
2
null
null
null
null
null
2
2
null
null
null
null
null
null
2
1
2
2
0
1
3
100,602
35
35,951
1
1
2
3
1
3
2
9
2
3
2
2
1
null
2
2
2
2
2
2
2
2
2
2
2
2
1
null
null
null
null
2
2
null
null
null
null
null
40
1
2
2
1
6
0
1
5
100,605
46
46,902
2
1
2
4
1
null
2
8
3
4
4
1
3
null
1
2
2
2
2
2
2
2
null
null
2
1
2
null
null
null
null
null
2
2
null
null
null
null
50
1
1
1
2
2
0
1
5
100,617
12
12,903
1
2
1
4
2
3
1
7
1
2
null
1
1
null
2
1
2
1
2
2
2
2
null
1
2
1
2
null
3.5
null
null
null
2
null
null
null
null
null
null
null
2
1
2
2
3
2
1
100,628
35
35,941
2
6
2
1
null
1
2
9
2
3
null
2
3
2
null
2
2
null
9
2
2
2
null
null
null
null
1
null
null
null
null
null
null
null
null
null
null
null
null
null
null
1
2
6
0
2
2
100,642
35
35,934
2
1
2
4
2
2
4
9
2
2
null
3
null
null
null
2
1
1
2
2
2
2
2
1
null
1
2
null
null
null
null
null
2
null
null
null
null
null
null
null
2
1
2
6
0
2
null
100,652
10
10,911
1
1
2
5
null
2
2
10
3
2
null
4
1
null
2
2
1
2
1
2
2
2
1
1
1
1
2
null
null
null
null
null
2
null
null
null
null
null
null
null
2
1
2
6
2
1
5
100,667
12
12,902
2
1
2
2
1
null
2
8
3
3
3
2
1
null
2
2
2
2
2
2
2
2
2
2
2
2
1
null
2.7
null
null
null
2
null
null
null
null
null
40
1
2
1
2
1
0
1
5
100,685
48
48,932
1
1
2
4
1
3
3
9
2
2
4
3
1
null
2
2
2
2
2
2
2
2
2
1
2
2
1
null
4.1
null
null
null
2
2
180
0
0
0
40
1
2
2
1
6
0
1
5
10,070
35
35,975
2
1
2
2
1
3
1
10
1
1
3
1
3
null
1
2
2
2
2
2
2
2
2
2
2
2
1
null
null
null
null
null
2
null
null
null
null
null
30
1
2
1
2
6
0
1
5
100,719
59
59,951
1
1
2
5
null
3
4
8
4
3
null
2
1
null
2
2
2
2
2
2
2
2
2
1
2
1
2
null
null
null
2
null
2
2
null
null
null
null
null
null
2
1
2
6
null
1
4
10,073
59
59,923
2
1
2
4
2
3
2
7
3
3
null
1
3
null
1
2
2
2
2
2
2
1
2
1
2
2
2
null
null
null
2
null
2
2
null
null
null
null
null
null
2
1
2
6
null
2
2
100,732
35
35,949
1
2
1
5
null
null
2
7
1
2
null
1
3
null
1
2
1
2
2
2
2
2
2
2
2
1
1
null
null
null
null
0
2
null
null
null
null
null
null
null
2
2
1
6
0
2
2
100,763
59
59,933
2
1
2
5
null
3
1
10
1
1
null
1
3
null
2
2
2
2
2
2
2
2
2
2
2
2
1
null
null
null
2
null
2
2
null
null
null
null
null
null
2
1
2
6
null
2
5
100,777
35
35,942
2
2
1
3
1
3
3
8
2
3
5
2
3
null
2
2
2
2
2
2
2
2
2
null
2
2
1
null
null
25
null
0
1
null
null
null
null
null
20
2
1
1
2
6
3
1
3
100,795
24
24,907
2
1
2
5
null
2
2
9
1
2
null
2
3
null
2
2
2
1
2
2
2
2
2
1
2
2
1
2
null
null
2
0
2
null
null
null
null
null
null
null
2
1
2
6
0
null
5
100,798
35
35,955
2
2
2
5
null
3
2
9
1
2
null
3
null
null
null
2
2
2
1
2
2
2
2
2
2
2
1
null
null
null
null
0
2
null
null
null
null
null
null
null
2
1
2
6
0
2
5
1,008
48
48,931
2
6
2
1
null
null
2
8
2
1
null
1
1
null
null
2
2
null
2
2
2
2
2
null
2
2
1
null
5.3
null
null
null
2
2
null
null
null
null
null
null
2
2
1
6
0
1
4
100,803
24
24,906
1
2
1
4
1
3
1
9
1
3
3
2
3
null
1
2
2
1
2
2
2
2
2
2
2
2
1
2
null
null
2
null
2
null
null
null
null
null
50
1
2
1
2
6
0
1
5
100,811
48
48,934
1
1
2
5
null
3
1
7
1
2
null
1
3
null
1
2
1
1
2
2
2
2
2
2
2
2
1
null
2.8
null
null
null
2
2
60
360
30
0
null
null
2
1
2
6
0
1
5
100,815
35
35,957
2
1
2
3
1
3
4
8
4
3
3
1
1
null
2
2
2
2
2
2
1
1
2
null
2
1
2
null
null
null
null
null
1
null
null
null
null
null
35
1
null
1
2
6
0
1
5
100,831
24
24,913
2
2
2
3
2
3
3
7
3
3
null
null
3
null
2
2
2
1
9
2
2
2
2
null
2
2
1
1
null
null
2
0
2
null
null
null
null
null
null
null
2
2
1
6
2
2
1
100,848
59
59,923
2
2
1
4
2
2
4
5
3
4
null
3
1
null
2
2
1
2
2
2
1
2
2
2
2
1
2
null
null
null
2
null
1
2
null
null
null
null
null
null
2
1
2
6
null
2
1
100,874
47
47,907
2
1
2
5
null
3
2
9
1
3
null
2
3
null
1
2
2
1
2
2
2
2
null
1
2
1
2
null
3.7
null
null
null
2
2
0
0
0
0
null
null
2
2
1
2
0
1
3
100,876
59
59,952
1
2
1
5
null
2
2
9
2
1
null
1
3
null
2
2
2
2
2
2
2
2
2
2
2
2
1
null
null
null
2
null
2
2
null
null
null
null
null
null
2
1
2
6
null
2
4
100,890
48
48,935
1
1
1
3
1
3
1
10
2
3
3
1
3
null
2
2
2
2
2
2
2
2
2
1
2
2
1
null
6.6
null
null
null
2
2
0
0
null
120
45
1
2
1
2
6
0
2
5
10,091
48
48,935
1
2
2
2
1
3
2
9
1
2
3
2
3
null
1
2
2
2
2
2
2
2
2
2
2
2
1
null
2.4
null
null
null
2
2
0
0
300
0
null
null
2
1
2
6
0
null
5
100,919
48
48,935
2
2
1
5
null
1
2
7
3
3
null
2
1
null
2
2
2
2
2
2
2
2
1
null
2
2
1
null
1.7
20
null
null
2
2
0
0
90
0
null
null
2
1
2
6
1
1
3
100,926
60
60,901
1
6
2
1
null
2
1
10
2
1
null
1
3
1
null
2
2
null
2
2
2
2
2
null
2
2
1
null
null
null
null
null
2
null
null
null
null
null
null
null
2
1
2
1
0
1
5
100,931
48
48,931
1
2
2
3
1
3
2
8
1
5
5
1
1
null
2
2
2
2
2
2
2
2
2
2
2
2
1
null
5.6
null
null
null
1
2
420
0
0
5
50
1
2
1
2
6
0
null
4
100,939
24
24,903
1
1
2
4
1
3
2
9
2
4
3
3
3
null
1
2
2
1
2
2
2
2
2
2
2
2
2
2
null
null
2
null
2
null
null
null
null
null
40
1
2
1
2
6
0
1
5
100,943
10
10,912
1
2
2
5
null
3
3
9
2
2
null
1
3
null
2
2
2
2
2
2
2
2
2
2
2
2
1
null
null
null
null
null
2
null
null
null
null
null
null
null
2
1
2
6
0
2
5
100,949
46
46,901
2
1
2
3
1
3
2
9
2
3
3
2
1
null
2
2
2
2
2
2
2
2
2
2
2
2
2
null
null
null
null
null
2
2
null
null
null
null
50
1
2
1
2
1
0
1
5
100,956
24
24,903
2
2
1
2
1
3
3
7
3
4
4
2
1
null
2
2
2
2
2
2
null
1
2
2
2
1
2
1
null
null
2
0
2
null
null
null
null
null
30
1
2
1
2
6
0
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End of preview. Expand in Data Studio

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

This README.md presents numerous variable that I think correlate with levels of life satisfaction and stress.

----------------------------------------–------------------------------------------------------------------------------------

                        ------------------------Faze 1: Finding Data set------------------------

First, I imported a dataset that containd data from a healthcare Survey that was conducted in 2019 in Canada. It includes data about weekly wages, work hours, eating habits, smoking habits, and of course life satisfaction and stress levels, and many more. Kaggle original dataset: https://www.kaggle.com/datasets/aradhanahirapara/healthcare-survey/data

The description of each varible: The mapping of code-to-value are explained in the PDF file in the project directory

Column Name Description
ADM_RNO1 Unique administrative record number
Province Province where the respondent resides (see encoding guide below)
Health_region_grouped Grouped health region of residence
Gender Respondent's gender
Marital_status Current marital status
Household Type of household composition
Age Age of respondent (years)
Worked_job_business Whether the respondent worked at a job or business in the past 12 months
Edu_level Highest level of education attained
Gen_health_state General health self-assessment
Life_satisfaction Self-rated life satisfaction
Mental_health_state Self-rated mental health status
Stress_level Perceived stress level in daily life
Work_stress Stress experienced at work
Sense_belonging Sense of belonging to community
Weight_state Weight status based on BMI categories
BMI_12_17 Body Mass Index for respondents aged 12-17
BMI_18_above Body Mass Index for respondents aged 18 and above
Sleep_apnea Diagnosed with sleep apnea (yes/no)
High_BP Diagnosed with high blood pressure (yes/no)
High_cholesterol Diagnosed with high cholesterol (yes/no)
Diabetic Diagnosed with diabetes (yes/no)
Fatigue_syndrome Diagnosed with chronic fatigue syndrome (yes/no)
Mood_disorder Diagnosed with a mood disorder (yes/no)
Anxiety_disorder Diagnosed with an anxiety disorder (yes/no)
Respiratory_chronic_con Diagnosed with chronic respiratory condition (yes/no)
Musculoskeletal_con Diagnosed with musculoskeletal condition (yes/no)
Cardiovascular_con Diagnosed with cardiovascular condition (yes/no)
Health_utility_index Health Utility Index score
Pain_status Frequency and severity of pain
Act_improve_health Engaged in activities to improve health
Fruit_veg_con Daily consumption of fruits and vegetables
Smoked Smoking status
Tobacco_use Tobacco product consumption
Weekly_alcohol Frequency of alcohol consumption per week
Cannabis_use Cannabis use in the past 12 months
Drug_use Illicit drug use in the past 12 months
Total_active_time Total time spent on physical activities
Total_physical_act_time Total duration of physical activities per week
Other_physical_act_time Time spent on other physical activities
Physical_vigorous_act_time Time spent on vigorous physical activity
Work_hours Number of hours worked per week
Working_status Employment status (employed, unemployed, retired, etc.)
Aboriginal_identity Whether the respondent identifies as Aboriginal
Birth_country Country of birth
Immigrant Immigration status
Insurance_cover Type of health insurance coverage
Food_security Household food security status
Income_source Primary source of income
Total_income Total annual income

----------------------------------------–------------------------------------------------------------------------------------

                       ----------------------------Faze 2: Data cleaning----------------------------

It was assentianl to investigate the structure of the data, and to see what defects it has, in terms of outliers, and coding problems.
So to start breaking down the distributions, I created graphs that show distribution of raw data and marked outliers of only the large scale values (not the ones with fixed 1-10 degrees) image-12 image-13 image-14 image-15 image-16 image-17 image-18

----------------------------------------–------------------------------------------------------------------------------------

                        ----------------Faze 3: Analyze the data, and create insights:----------------

Initially I wanted to see how the income distributes across the regions, and how it correlates with life satisfaction, general stress levels, and work hours.

Insights from these plots are to establish the connection between the total income ($) each variable: The people's income distributes somewhat evenly between the regions: image

We can learn that most individuals reports average stress levels, across all income groups. With a correlation of 0.058, Positive low: image-1

We can see life satisfaction correlates with income to a level of 0.171, a Positive low, although the highest so far, indicating what is presumed to be obvious. People who earn more, tend to be happier image-2

Work hours to income, correlation is 0.136, Positive low. This inidcates a rather exopected result, basicly if you work harder, youre chances for a stable, and easy life are higher: image-3

Is there a relationship between work stress and menal health? The basic assumption would be that there is. First we want to see if people that work a lot tend to report high life satisfaction: image-4

Do people with low stress levels, report high satisfaction? We can see close scores although women's report less satisfaction an average from life as levels of stress go up. could be explained by the general tendency of women to be more emotional. Some what predicted results, although stressfull life do not necessarily have an affect over all satisfaction from life: image-5

Here we have a bar plot that shows average stress levels to weekly work hours, divided by gender, also below we can see the correlations between the two variable, both of which are positive and close to zero, implying a lower correlation. Another insight from the graph indicates that women tend to report higher stress, compared to men, on 95% of work hour groups.

------- Over all ρ (Work_hours vs Stress_level) = 0.10, Positive low

------- Male ρ (Work_hours vs Stress_level) = 0.12, Positive low

------- Female ρ (Work_hours vs Stress_level) = 0.12, Positive low image-6

Now let's see what connection of smoking habits on cardiovascular / blood pressure. correlation Smoking habits to high_bp = -0.173, Negative low correlation Smoking habits to Cardiovascular condition = -0.125, Negative low on average we can see less effects of smoking habits on health, but a slight correlation. image-7

Smoker/Non-smoker to Stress levels: smokers tend to be a bit more stressfull, which do indicates on life satisfaction. image-8

Do people with healthier nutrition, that eat fruits and veggies, report less stress and higher life satisfaction? The results show almost no correlation between eating fruits and veggies, and stress and life satisfaction: image-9 image-10

Cannabies use. Canada is known for its legaliezd marijuana, which creats a large population that uses the drug, either on perscription, or just for fun. Were gonna see how it effects daily stress: we can see that medical use usually is stronger, and helps more on stress... image-11

The conclusion from these analytics are to show that stress and life satisfaction, as hard to messeure as can be, are affected by many variables, and correlate with the them to a level that suggests connection.

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Links: Huggingface repository: https://huggingface.co/datasets/LevyJonas/health_care_survay

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