Maximum policy holders are between the ages of 18-27. Males hold slightly more policies than females. Most policy holders have a BMI around 30 and do not have any children. The majority of policy holders do not smoke, and Southeast region has the highest number of policy holders. Insurance charges vary between 1,121-8,700 INR annually. Charges are most closely correlated with age compared to BMI and number of children.

1. INSURANCE ASSIGNMENT
Submitted to:- Prof. Vinod Sir

2. Age of the policy Holder
In [3]: insurance.age.value_counts()
...: sns.histplot(x='age', data=insurance,color='r')
...: plt.title('Age of Policy Holders',fontsize=15)
...: plt.xlabel('Age',fontsize=15)
Out[3]: Text(0.5, 0, 'Age')
Insights:- Maximum Policy Holders age is b/w 18 to
27

3. Sex Gender (Male & Females)
In [5]: insurance.sex.value_counts()
Out[5]:
male 676
female 662
Name: sex, dtype: int64
 In [6]: sns.countplot(x='sex',data=insurance)
 ...: plt.title('No of Males and
Female',fontsize=15)
 Out[6]: Text(0.5, 1.0, 'No of Males and
Female')
Insights:- Male Policy Holders are slightly on the higher
than Female

4. BMI of Policy Holders
In [3]: insurance.bmi.value_counts()
Out[3]:
32.300 13
28.310 9
30.495 8
30.875 8
31.350 8
..
46.200 1
23.800 1
44.770 1
32.120 1
30.970 1
Name: bmi, Length: 548, dtype:
In [4]: sns.histplot(x='bmi',
data=insurance,color='r')
...: plt.title('No of Policy Holders
wrt bmi',fontsize=15)
...: plt.xlabel('bmi',fontsize=15)
Insights:- Maximum Policy Holders have BMI index around
30

5. No of Children in the family of Policy Holders
In [6]: insurance.children.value_counts()
Out[6]:
0 574
1 324
2 240
3 157
4 25
5 18
Name: children, dtype: int64
In [7]: sns.countplot(x='children',data=insurance)
...: plt.title('No of Policy holders wrt to Children',fontsize=15)
Out[7]: Text(0.5, 1.0, 'No of Policy holders wrt to Children')
Insights:- With this Plot we can conclude that the Maximum Policy Holders have no children

6. Smoker and Non Smoker (Male & Female)
In [8]: insurance.smoker.value_counts()
Out[8]:
no 1064
yes 274
Name: smoker, dtype: int64
In [9]: sns.countplot(x='smoker',data=insurance)
...: plt.title('Smoker and Non Smoker',fontsize=15)
Out[9]: Text(0.5, 1.0, 'Smoker and Non Smoker’)
In [10]: pd.crosstab(insurance.sex , insurance.smoker)
Out[10]:
smoker no yes
sex
female 547 115
male 517 159
Insights:- With these Plot we got a outcome that Non smokers are is around 80% as compared to smoker and
among them only 9% are Female smoker as compared to men which are 12%

7. Regions of Policy Holder
In [11]: insurance.region.value_counts()
Out[11]:
southeast 364
southwest 325
northwest 325
northeast 324
Name: region, dtype: int64
In [12]: sns.countplot(x='region',data=insurance)
...: plt.title('Policy holders from different region of
India',fontsize=15)
Out[12]: Text(0.5, 1.0, 'Policy holders from different region of
India')
Insights:- Maximum policy Holders came from South East Region

8. Insurance charges paid by Policy Holder
In [13]: insurance.charges.value_counts()
Out[13]:
1639.56310 2
16884.92400 1
29330.98315 1
2221.56445 1
19798.05455 1
..
7345.08400 1
26109.32905 1
28287.89766 1
1149.39590 1
29141.36030 1
Name: charges, Length: 1337, dtype: int64
In [14]: sns.histplot(x='charges', data=insurance,color='r')
...: plt.title('Yearly Charges for Insurance among Policy holders',fontsize=15)
...: plt.xlabel('charges',fontsize=15)
Out[14]: Text(0.5, 0, 'charges')
Insights:- Max Insurance Charges Varies b/w 1121 Rs to 8700 Rs

9. Charges Connection with Age , BMI or Children
In [5]: da=insurance[['charges','age','bmi']]
...: sns.pairplot(da,kind='reg')
Out[5]: <seaborn.axisgrid.PairGrid at 0x23ca1412f70>
In [6]: print(da.corr())
charges age bmi
charges 1.000000 0.299008 0.198341
age 0.299008 1.000000 0.109272
bmi 0.198341 0.109272 1.000000
In [7]: sns.heatmap(da.corr(),cmap="YlGnBu", annot=True)
Out[7]: <AxesSubplot:>

10. In [9]: da=insurance[['charges','age','children']]
...: sns.pairplot(da,kind='reg')
Out[9]: <seaborn.axisgrid.PairGrid at 0x23ca6259250>
In [12]: print(da.corr())
charges age children
charges 1.000000 0.299008 0.067998
age 0.299008 1.000000 0.042469
children 0.067998 0.042469 1.000000
In [13]: sns.heatmap(da.corr(),cmap="YlGnBu", annot=True)
Out[13]: <AxesSubplot:>
Insights:- With this Plot we can conclude that Charges
and age is closely connected to each other with there
ratio 0.3 which is comparatively higher than BMI and
Children with ratio 0.2 and 0.068 respectively