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Project 2 - EDA on Vehicle Insurance Customer Data

1.Question¶ import pandas as pdimport numpy as np Customer_Detail=pd.read_csv('customer_details.csv')Customer_Policy=pd.read_csv('customer_policy_details.csv') Customer_Detail.columns=['Customer_id','Gender','Age','Driving Licence Present','Region Code','Previously Insured','Vehicle age','Vehicle Damage'] Customer_Policy.columns=['Customer_id','Annual…

PYTHON

Sushant Ovhal
updated on 20 Aug 2022

1.Question¶

import pandas as pd
import numpy as np

Customer_Detail=pd.read_csv('customer_details.csv')
Customer_Policy=pd.read_csv('customer_policy_details.csv')

Customer_Detail.columns=['Customer_id','Gender','Age','Driving Licence Present','Region Code','Previously Insured','Vehicle age','Vehicle Damage']

Customer_Policy.columns=['Customer_id','Annual Premium(in Rs)','Sales Channel code','Vintage', 'Response']

Customer_Detail

Ans:

Customer_id	Gender	Age	Driving Licence Present	Region Code	Previously Insured	Vehicle age	Vehicle Damage
0	1.0	Male	44.0	1.0	28.0	0.0	> 2 Years	Yes
1	2.0	Male	76.0	1.0	3.0	0.0	1-2 Year	No
2	3.0	Male	47.0	1.0	28.0	0.0	> 2 Years	Yes
3	4.0	Male	21.0	1.0	11.0	1.0	< 1 Year	No
4	5.0	Female	29.0	1.0	41.0	1.0	< 1 Year	No
...	...	...	...	...	...	...	...	...
381104	381105.0	Male	74.0	1.0	26.0	1.0	1-2 Year	No
381105	381106.0	Male	30.0	1.0	37.0	1.0	< 1 Year	No
381106	381107.0	Male	21.0	1.0	30.0	1.0	< 1 Year	No
381107	381108.0	Female	68.0	1.0	14.0	0.0	> 2 Years	Yes
381108	381109.0	Male	46.0	1.0	29.0	0.0	1-2 Year	No

381109 rows × 8 columns

Customer_Policy

Ans:

Customer_id	Annual Premium(in Rs)	Sales Channel code	Vintage	Response
0	1.0	40454.0	26.0	217.0	1.0
1	2.0	33536.0	26.0	183.0	0.0
2	3.0	38294.0	26.0	27.0	1.0
3	4.0	28619.0	152.0	203.0	0.0
4	5.0	27496.0	152.0	39.0	0.0
...	...	...	...	...	...
381104	381105.0	30170.0	26.0	88.0	0.0
381105	381106.0	40016.0	152.0	131.0	0.0
381106	381107.0	35118.0	160.0	161.0	0.0
381107	381108.0	44617.0	124.0	74.0	0.0
381108	381109.0	41777.0	26.0	237.0	0.0

381109 rows × 5 columns

2.Question

Customer_Detail.isnull().sum()

Ans:

Customer_id                386
Gender                     368
Age                        368
Driving Licence Present    393
Region Code                392
Previously Insured         381
Vehicle age                381
Vehicle Damage             407
dtype: int64


Customer_Detail.fillna(Customer_Detail.mean())

Ans:

Customer_id	Gender	Age	Driving Licence Present	Region Code	Previously Insured	Vehicle age	Vehicle Damage
0	1.0	Male	44.0	1.0	28.0	0.0	> 2 Years	Yes
1	2.0	Male	76.0	1.0	3.0	0.0	1-2 Year	No
2	3.0	Male	47.0	1.0	28.0	0.0	> 2 Years	Yes
3	4.0	Male	21.0	1.0	11.0	1.0	< 1 Year	No
4	5.0	Female	29.0	1.0	41.0	1.0	< 1 Year	No
...	...	...	...	...	...	...	...	...
381104	381105.0	Male	74.0	1.0	26.0	1.0	1-2 Year	No
381105	381106.0	Male	30.0	1.0	37.0	1.0	< 1 Year	No
381106	381107.0	Male	21.0	1.0	30.0	1.0	< 1 Year	No
381107	381108.0	Female	68.0	1.0	14.0	0.0	> 2 Years	Yes
381108	381109.0	Male	46.0	1.0	29.0	0.0	1-2 Year	No

381109 rows × 8 columns

Customer_Policy.isnull().sum()
 Ans:
 

Customer_id              387
Annual Premium(in Rs)    346
Sales Channel code       400
Vintage                  388
Response                 361
dtype: int64

 
Customer_Policy.fillna(Customer_Policy.mean())

Ans:

	Customer_id	Annual Premium(in Rs)	Sales Channel code	Vintage	Response
0	1.0	40454.0	26.0	217.0	1.0
1	2.0	33536.0	26.0	183.0	0.0
2	3.0	38294.0	26.0	27.0	1.0
3	4.0	28619.0	152.0	203.0	0.0
4	5.0	27496.0	152.0	39.0	0.0
...	...	...	...	...	...
381104	381105.0	30170.0	26.0	88.0	0.0
381105	381106.0	40016.0	152.0	131.0	0.0
381106	381107.0	35118.0	160.0	161.0	0.0
381107	381108.0	44617.0	124.0	74.0	0.0
381108	381109.0	41777.0	26.0	237.0	0.0

381109 rows × 5 columns

Customer_Detail['Gender'].fillna(Customer_Detail['Gender'].mode()[0],inplace=True

Customer_Detail['Vehicle age'].fillna(Customer_Detail['Vehicle age'].mode()[0],inplace=True)

Customer_Detail['Region Code'].fillna(Customer_Detail['Region Code'].mode()[0],inplace=True)

Customer_Policy.fillna(Customer_Policy).mode()
 Ans:
 

	Customer_id	Annual Premium(in Rs)	Sales Channel code	Vintage	Response
0	1.0	2630.0	152.0	256.0	0.0
1	2.0	NaN	NaN	NaN	NaN
2	3.0	NaN	NaN	NaN	NaN
3	4.0	NaN	NaN	NaN	NaN
4	5.0	NaN	NaN	NaN	NaN
...	...	...	...	...	...
380717	381105.0	NaN	NaN	NaN	NaN
380718	381106.0	NaN	NaN	NaN	NaN
380719	381107.0	NaN	NaN	NaN	NaN
380720	381108.0	NaN	NaN	NaN	NaN
380721	381109.0	NaN	NaN	NaN	NaN

380722 rows × 5 columns

Customer_Detail.dropna(subset=["Customer_id"],inplace=True)

Customer_Policy.dropna(subset=["Customer_id"],inplace=True)
 

ii outlier

Q1=Customer_Detail.quantile(0.25)
Q3=Customer_Policy.quantile(0.75)

print(Q1)

Ans:

Customer_id                95269.5
Age                           25.0
Driving Licence Present        1.0
Region Code                   15.0
Previously Insured             0.0
Name: 0.25, dtype: float64

print(Q3)

Ans:

Customer_id              285818.75
Annual Premium(in Rs)     39401.75
Sales Channel code          152.00
Vintage                     227.00
Response                      0.00
Name: 0.75, dtype: float64

IQR=Q3-Q1
lower=Q1-(1.5*IQR)
upper=Q3+(1.5*IQR) 

Customer_Detail[(Customer_Detail<lower) | (Customer_Detail>upper)].sum()

Customer_Policy[(Customer_Policy<lower) | (Customer_Policy>upper)].sum()

Customer_Detail.describe()
 Ans:

	Customer_id	Age	Driving Licence Present	Region Code	Previously Insured
count	380723.000000	380357.000000	380331.000000	380723.000000	380342.000000
mean	190548.776244	38.822788	0.997868	26.391090	0.458259
std	110016.805160	15.512284	0.046128	13.223772	0.498255
min	1.000000	20.000000	0.000000	0.000000	0.000000
25%	95269.500000	25.000000	1.000000	15.000000	0.000000
50%	190543.000000	36.000000	1.000000	28.000000	0.000000
75%	285822.500000	49.000000	1.000000	35.000000	1.000000
max	381109.000000	85.000000	1.000000	52.000000	1.000000

Customer_Policy.describe()
 Ans:

	Customer_id	Annual Premium(in Rs)	Sales Channel code	Vintage	Response
count	380722.000000	380378.000000	380322.000000	380334.000000	380361.000000
mean	190547.491663	30563.999774	112.036687	154.347192	0.122526
std	110013.824148	17197.918886	54.205529	83.670742	0.327892
min	1.000000	2630.000000	1.000000	10.000000	0.000000
25%	95276.250000	24407.000000	29.000000	82.000000	0.000000
50%	190536.500000	31667.000000	133.000000	154.000000	0.000000
75%	285818.750000	39401.750000	152.000000	227.000000	0.000000
max	381109.000000	540165.000000	163.000000	299.000000	1.000000

Customer_Policy['Annual Premium(in Rs)']=Customer_Policy['Annual Premium(in Rs)'].astype('float') 

Q1=Customer_Policy[['Annual Premium(in Rs)']].quantile(0.25)
Q2=Customer_Policy[['Annual Premium(in Rs)']].quantile(0.75)

((Customer_Policy[['Annual Premium(in Rs)']]<lower) | (Customer_Policy[['Annual Premium(in Rs)']]>upper)).sum

Customer_Policy['Response']=Customer_Policy['Response'].astype('float')

Q1=Customer_Policy[['Response']].quantile(0.25)
Q2=Customer_Policy[['Response']].quantile(0.75)

((Customer_Policy[['Response']]<lower)) | (Customer_Policy[['Response']]>upper).sum()
 

iii WhiteSpaces

Customer_Detail.apply(lambda x:x.str.strip() if x.dtype=="object" else x)
Ans: 

	Customer_id	Gender	Age	Driving Licence Present	Region Code	Previously Insured	Vehicle age	Vehicle Damage
0	1.0	Male	44.0	1.0	28.0	0.0	> 2 Years	Yes
1	2.0	Male	76.0	1.0	3.0	0.0	1-2 Year	No
2	3.0	Male	47.0	1.0	28.0	0.0	> 2 Years	Yes
3	4.0	Male	21.0	1.0	11.0	1.0	< 1 Year	No
4	5.0	Female	29.0	1.0	41.0	1.0	< 1 Year	No
...	...	...	...	...	...	...	...	...
381104	381105.0	Male	74.0	1.0	26.0	1.0	1-2 Year	No
381105	381106.0	Male	30.0	1.0	37.0	1.0	< 1 Year	No
381106	381107.0	Male	21.0	1.0	30.0	1.0	< 1 Year	No
381107	381108.0	Female	68.0	1.0	14.0	0.0	> 2 Years	Yes
381108	381109.0	Male	46.0	1.0	29.0	0.0	1-2 Year	No

380723 rows × 8 columns

Customer_Policy.apply(lambda x: x.str.strip() if x.dtype=="object" else x)
 Ans:
 

	Customer_id	Annual Premium(in Rs)	Sales Channel code	Vintage	Response
0	1.0	40454.0	26.0	217.0	1.0
1	2.0	33536.0	26.0	183.0	0.0
2	3.0	38294.0	26.0	27.0	1.0
3	4.0	28619.0	152.0	203.0	0.0
4	5.0	27496.0	152.0	39.0	0.0
...	...	...	...	...	...
381104	381105.0	30170.0	26.0	88.0	0.0
381105	381106.0	40016.0	152.0	131.0	0.0
381106	381107.0	35118.0	160.0	161.0	0.0
381107	381108.0	44617.0	124.0	74.0	0.0
381108	381109.0	41777.0	26.0	237.0	0.0

380722 rows × 5 columns

iv Case correction

Customer_Detail.apply(lambda x:x.astype(str).str.upper())
Customer_Detail
Ans:

	Customer_id	Gender	Age	Driving Licence Present	Region Code	Previously Insured	Vehicle age	Vehicle Damage
0	1.0	Male	44.0	1.0	28.0	0.0	> 2 Years	Yes
1	2.0	Male	76.0	1.0	3.0	0.0	1-2 Year	No
2	3.0	Male	47.0	1.0	28.0	0.0	> 2 Years	Yes
3	4.0	Male	21.0	1.0	11.0	1.0	< 1 Year	No
4	5.0	Female	29.0	1.0	41.0	1.0	< 1 Year	No
...	...	...	...	...	...	...	...	...
381104	381105.0	Male	74.0	1.0	26.0	1.0	1-2 Year	No
381105	381106.0	Male	30.0	1.0	37.0	1.0	< 1 Year	No
381106	381107.0	Male	21.0	1.0	30.0	1.0	< 1 Year	No
381107	381108.0	Female	68.0	1.0	14.0	0.0	> 2 Years	Yes
381108	381109.0	Male	46.0	1.0	29.0	0.0	1-2 Year	No

380723 rows × 8 columns

convert nominal data into dummies

Customer_Detail2=pd.get_dummies(Customer_Detail)

Customer_Detail2
 
Ans:

	Customer_id	Age	Driving Licence Present	Region Code	Previously Insured	Gender_Female	Gender_Male	Vehicle age_1-2 Year	Vehicle age_< 1 Year	Vehicle age_> 2 Years	Vehicle Damage_No	Vehicle Damage_Yes
0	1.0	44.0	1.0	28.0	0.0	0	1	0	0	1	0	1
1	2.0	76.0	1.0	3.0	0.0	0	1	1	0	0	1	0
2	3.0	47.0	1.0	28.0	0.0	0	1	0	0	1	0	1
3	4.0	21.0	1.0	11.0	1.0	0	1	0	1	0	1	0
4	5.0	29.0	1.0	41.0	1.0	1	0	0	1	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...
381104	381105.0	74.0	1.0	26.0	1.0	0	1	1	0	0	1	0
381105	381106.0	30.0	1.0	37.0	1.0	0	1	0	1	0	1	0
381106	381107.0	21.0	1.0	30.0	1.0	0	1	0	1	0	1	0
381107	381108.0	68.0	1.0	14.0	0.0	1	0	0	0	1	0	1
381108	381109.0	46.0	1.0	29.0	0.0	0	1	1	0	0	1	0

380723 rows × 12 columns

Customer_Policy2=pd.get_dummies(Customer_Policy) 

Customer_Policy2
 Ans:
 

	Customer_id	Annual Premium(in Rs)	Sales Channel code	Vintage	Response
0	1.0	40454.0	26.0	217.0	1.0
1	2.0	33536.0	26.0	183.0	0.0
2	3.0	38294.0	26.0	27.0	1.0
3	4.0	28619.0	152.0	203.0	0.0
4	5.0	27496.0	152.0	39.0	0.0
...	...	...	...	...	...
381104	381105.0	30170.0	26.0	88.0	0.0
381105	381106.0	40016.0	152.0	131.0	0.0
381106	381107.0	35118.0	160.0	161.0	0.0
381107	381108.0	44617.0	124.0	74.0	0.0
381108	381109.0	41777.0	26.0	237.0	0.0

380722 rows × 5 columns

Drop duplicate

Customer_Detail2.drop_duplicates()

Ans:

	Customer_id	Age	Driving Licence Present	Region Code	Previously Insured	Gender_Female	Gender_Male	Vehicle age_1-2 Year	Vehicle age_< 1 Year	Vehicle age_> 2 Years	Vehicle Damage_No	Vehicle Damage_Yes
0	1.0	44.0	1.0	28.0	0.0	0	1	0	0	1	0	1
1	2.0	76.0	1.0	3.0	0.0	0	1	1	0	0	1	0
2	3.0	47.0	1.0	28.0	0.0	0	1	0	0	1	0	1
3	4.0	21.0	1.0	11.0	1.0	0	1	0	1	0	1	0
4	5.0	29.0	1.0	41.0	1.0	1	0	0	1	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...
381104	381105.0	74.0	1.0	26.0	1.0	0	1	1	0	0	1	0
381105	381106.0	30.0	1.0	37.0	1.0	0	1	0	1	0	1	0
381106	381107.0	21.0	1.0	30.0	1.0	0	1	0	1	0	1	0
381107	381108.0	68.0	1.0	14.0	0.0	1	0	0	0	1	0	1
381108	381109.0	46.0	1.0	29.0	0.0	0	1	1	0	0	1	0

380723 rows × 12 columns

Customer_Policy2.drop_duplicates()

	Customer_id	Annual Premium(in Rs)	Sales Channel code	Vintage	Response
0	1.0	40454.0	26.0	217.0	1.0
1	2.0	33536.0	26.0	183.0	0.0
2	3.0	38294.0	26.0	27.0	1.0
3	4.0	28619.0	152.0	203.0	0.0
4	5.0	27496.0	152.0	39.0	0.0
...	...	...	...	...	...
381104	381105.0	30170.0	26.0	88.0	0.0
381105	381106.0	40016.0	152.0	131.0	0.0
381106	381107.0	35118.0	160.0	161.0	0.0
381107	381108.0	44617.0	124.0	74.0	0.0
381108	381109.0	41777.0	26.0	237.0	0.0

380722 rows × 5 columns

3.Question

Master_Table=Customer_Detail2.merge(Customer_Policy2,on=["Customer_id"])

Master_Table
 Ans:
 

	Customer_id	Age	Driving Licence Present	Region Code	Previously Insured	Gender_Female	Gender_Male	Vehicle age_1-2 Year	Vehicle age_< 1 Year	Vehicle age_> 2 Years	Vehicle Damage_No	Vehicle Damage_Yes	Annual Premium(in Rs)	Sales Channel code	Vintage	Response
0	1.0	44.0	1.0	28.0	0.0	0	1	0	0	1	0	1	40454.0	26.0	217.0	1.0
1	2.0	76.0	1.0	3.0	0.0	0	1	1	0	0	1	0	33536.0	26.0	183.0	0.0
2	3.0	47.0	1.0	28.0	0.0	0	1	0	0	1	0	1	38294.0	26.0	27.0	1.0
3	4.0	21.0	1.0	11.0	1.0	0	1	0	1	0	1	0	28619.0	152.0	203.0	0.0
4	5.0	29.0	1.0	41.0	1.0	1	0	0	1	0	1	0	27496.0	152.0	39.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
380331	381105.0	74.0	1.0	26.0	1.0	0	1	1	0	0	1	0	30170.0	26.0	88.0	0.0
380332	381106.0	30.0	1.0	37.0	1.0	0	1	0	1	0	1	0	40016.0	152.0	131.0	0.0
380333	381107.0	21.0	1.0	30.0	1.0	0	1	0	1	0	1	0	35118.0	160.0	161.0	0.0
380334	381108.0	68.0	1.0	14.0	0.0	1	0	0	0	1	0	1	44617.0	124.0	74.0	0.0
380335	381109.0	46.0	1.0	29.0	0.0	0	1	1	0	0	1	0	41777.0	26.0	237.0	0.0

380336 rows × 16 columns

4.Question

Master_Table.groupby(['Gender_Male']).mean()['Annual Premium(in Rs)']

Ans:

Gender_Male
0    30491.959528
1    30623.670581
Name: Annual Premium(in Rs), dtype: float64

Master_Table.groupby(['Gender_Female']).mean()['Annual Premium(in Rs)']
Ans:

Gender_Female
0    30623.670581
1    30491.959528
Name: Annual Premium(in Rs), dtype: float64

Master_Table.groupby(['Age']).mean()['Annual Premium(in Rs)']
 Ans:

Age
20.0    26920.517153
21.0    30564.476130
22.0    30824.039423
23.0    30688.699065
24.0    31184.306152
            ...     
81.0    31201.571429
82.0    37705.379310
83.0    31012.727273
84.0    35440.818182
85.0    29792.363636
Name: Annual Premium(in Rs), Length: 66, dtype: float64

Master_Table['Gender_Male'].value_counts()
 Ans:

1    205851
0    174485
Name: Gender_Male, dtype: int64

Master_Table['Gender_Female'].value_counts()
 Ans:

0    205851
1    174485
Name: Gender_Female, dtype: int64

Master_Table.groupby(['Vehicle age_1-2 Year']).mean()['Annual Premium(in Rs)']
 Ans:

Vehicle age_1-2 Year
0    30606.151077
1    30524.590052
Name: Annual Premium(in Rs), dtype: float64

Master_Table.groupby(['Vehicle age_< 1 Year']).mean()['Annual Premium(in Rs)']
 Ans:

Vehicle age_< 1 Year
0    30903.960198
1    30115.361397
Name: Annual Premium(in Rs), dtype: float64

Master_Table.groupby(['Vehicle age_> 2 Years']).mean()['Annual Premium(in Rs)']
Ans:

Vehicle age_> 2 Years
0    30340.043557
1    35661.355606
Name: Annual Premium(in Rs), dtype: float64

5.Question

import numpy as np

Master_Table.corr()
 Ans:

	Customer_id	Age	Driving Licence Present	Region Code	Previously Insured	Gender_Female	Gender_Male	Vehicle age_1-2 Year	Vehicle age_< 1 Year	Vehicle age_> 2 Years	Vehicle Damage_No	Vehicle Damage_Yes	Annual Premium(in Rs)	Sales Channel code	Vintage	Response
Customer_id	1.000000	0.001669	-0.000475	-0.000727	0.002425	-0.001153	0.001153	0.001489	-0.001337	-0.000406	0.001523	-0.001436	0.003087	-0.002922	-0.000603	-0.001349
Age	0.001669	1.000000	-0.079770	0.042524	-0.254664	-0.145127	0.145127	0.692334	-0.787152	0.220577	-0.267226	0.267260	0.067778	-0.577861	-0.001218	0.111186
Driving Licence Present	-0.000475	-0.079770	1.000000	-0.001062	0.014959	0.018318	-0.018318	-0.037506	0.040221	-0.005968	0.016550	-0.016649	-0.012027	0.043864	-0.000851	0.010303
Region Code	-0.000727	0.042524	-0.001062	1.000000	-0.024604	-0.000503	0.000503	0.037779	-0.043952	0.014508	-0.027960	0.028062	-0.010700	-0.042414	-0.002752	0.010471
Previously Insured	0.002425	-0.254664	0.014959	-0.024604	1.000000	0.081909	-0.081909	-0.278771	0.358379	-0.191216	0.823324	-0.823328	0.004440	0.219312	0.002397	-0.341191
Gender_Female	-0.001153	-0.145127	0.018318	-0.000503	0.081909	1.000000	-1.000000	-0.147153	0.165755	-0.043059	0.091462	-0.091447	-0.003818	0.110926	0.002450	-0.052542
Gender_Male	0.001153	0.145127	-0.018318	0.000503	-0.081909	-1.000000	1.000000	0.147153	-0.165755	0.043059	-0.091462	0.091447	0.003818	-0.110926	-0.002450	0.052542
Vehicle age_1-2 Year	0.001489	0.692334	-0.037506	0.037779	-0.278771	-0.147153	0.147153	1.000000	-0.918807	-0.220431	-0.284118	0.284076	-0.002369	-0.507877	-0.002672	0.164036
Vehicle age_< 1 Year	-0.001337	-0.787152	0.040221	-0.043952	0.358379	0.165755	-0.165755	-0.918807	1.000000	-0.182465	0.369986	-0.369995	-0.022725	0.571054	0.002437	-0.209615
Vehicle age_> 2 Years	-0.000406	0.220577	-0.005968	0.014508	-0.191216	-0.043059	0.043059	-0.220431	-0.182465	1.000000	-0.206575	0.206703	0.062055	-0.146110	0.000633	0.109413
Vehicle Damage_No	0.001523	-0.267226	0.016550	-0.027960	0.823324	0.091462	-0.091462	-0.284118	0.369986	-0.206575	1.000000	-0.997867	-0.009196	0.223998	0.001908	-0.354015
Vehicle Damage_Yes	-0.001436	0.267260	-0.016649	0.028062	-0.823328	-0.091447	0.091447	0.284076	-0.369995	0.206703	-0.997867	1.000000	0.009259	-0.224008	-0.001960	0.353969
Annual Premium(in Rs)	0.003087	0.067778	-0.012027	-0.010700	0.004440	-0.003818	0.003818	-0.002369	-0.022725	0.062055	-0.009196	0.009259	1.000000	-0.113441	-0.000671	0.022295
Sales Channel code	-0.002922	-0.577861	0.043864	-0.042414	0.219312	0.110926	-0.110926	-0.507877	0.571054	-0.146110	0.223998	-0.224008	-0.113441	1.000000	-0.000151	-0.139071
Vintage	-0.000603	-0.001218	-0.000851	-0.002752	0.002397	0.002450	-0.002450	-0.002672	0.002437	0.000633	0.001908	-0.001960	-0.000671	-0.000151	1.000000	-0.001094
Response	-0.001349	0.111186	0.010303	0.010471	-0.341191	-0.052542	0.052542	0.164036	-0.209615	0.